Martin, T., Schön, T. B., & Allgöwer, F. (n.d.). Gaussian inference for data-driven state-feedback design of nonlinear systems. 22nd IFAC World Congress (Submitted), Preprint: ArXiv:2211.05639.

Zaverkin, V., Holzmüller, D., Bonfirraro, L., & Kästner, J. (2023). Transfer learning for chemically accurate interatomic neural network potentials. Phys. Chem. Chem. Phys., 25(7), 5383–5396. https://doi.org/10.1039/D₂CP05793J

Schlottke, A., Ibach, M., Steigerwald, J., & Weigand, B. (2023). Direct numerical simulation of a disintegrating liquid rivulet at a trailing edge. In W. E. Nagel, D. H. Kröner, & M. M. Resch (Eds.), High Performance Computing in Science and Engineering ’21 (pp. 239--257). Springer International Publishing. https://doi.org/10.1007/978-3-031-17937-2_14

Morato, M. M., Holicki, T., & Scherer, C. W. (2023). Stabilizing Model Predictive Control Synthesis using Integral Quadratic Constraints and Full-Block Multipliers. https://doi.org/10.48550/ARXIV.2210.03712

Monninger, T., Schmidt, J., Rupprecht, J., Raba, D., Jordan, J., Frank, D., Staab, S., & Dietmayer, K. (2023). SCENE: Reasoning about Traffic Scenes using Heterogeneous Graph Neural Networks. IEEE Robotics and Automation Letters, 1–8. https://doi.org/10.1109/LRA.2023.3234771

Kurz, M., Offenhäuser, P., & Beck, A. (2023). Deep reinforcement learning for turbulence modeling in large eddy simulations. International Journal of Heat and Fluid Flow, 99, 109094. https://doi.org/10.1016/j.ijheatfluidflow.2022.109094

Kneifl, J., Rosin, D., Röhrle, O., & Fehr, J. (2023). Low-dimensional Data-based Surrogate Model of a Continuum-mechanical Musculoskeletal System Based on Non-intrusive Model Order Reduction. arXiv. https://doi.org/10.48550/ARXIV.2302.06528

Kharitenko, A., & Scherer, C. (2023). Time-varying Zames–Falb multipliers for LTI Systems are superfluous. Automatica, 147, 110577. https://doi.org/10.1016/j.automatica.2022.110577

Kempf, D., Gao, M., Beck, A., Blind, M., Kopper, P., Kuhn, T., Kurz, M., Schwarz, A., & Munz, C.-D. (2023). Development of turbulent inflow methods for the high order HPC framework FLEXI. In W. E. Nagel, D. H. Kröner, & M. M. Resch (Eds.), High Performance Computing in Science and Engineering ’21 (pp. 289--304). Springer International Publishing. https://doi.org/10.1007/978-3-031-17937-2_17

Ibach, M., Steigerwald, J., & Weigand, B. (2023). Thixotropic effects in oscillating droplets. 11th International Conference on Multiphase Flow (ICMF), April 2–7, 2023, Kobe, Japan.

Hornischer, N. (2023). Model Order Reduction with Dynamically Transformed Modes for Electrophysiological Simulations. GAMM Archive for Students.

Holzmüller, D., & Bach, F. (2023). Convergence rates for non-log-concave sampling and log-partition estimation. ArXiv:2303.03237.

Holicki, T., & Scherer, C. W. (2023). Input-Output-Data-Enhanced Robust Analysis via Lifting. https://doi.org/10.48550/arXiv.2211.02149

Holicki, T., & Scherer, C. W. (2023). IQC Based Analysis and Estimator Design for Discrete-Time Systems Affected by Impulsive Uncertainties. https://doi.org/10.48550/arXiv.2212.08837

Herkert, R., Buchfink, P., Haasdonk, B., Rettberg, J., & Fehr, J. (2023). Randomized Symplectic Model Order Reduction for Hamiltonian Systems.

Grioui, F., & Blascheck, T. (2023). Heart Rate Visualizations on a Virtual Smartwatch to Monitor Physical Activity Intensity. Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. https://doi.org/10.5220/0011665500003417

Gander, M. J., Lunowa, S. B., & Rohde, C. (2023). Non-Overlapping Schwarz Waveform-Relaxation for Nonlinear Advection-Diffusion Equations. SIAM J. Sci. Comput., 45(1), A49–A73. https://doi.org/10.1137/21M1415005

Martin, T., Schön, T. B., & Allgöwer, F. (n.d.). Gaussian inference for data-driven state-feedback design of nonlinear systems. 22nd IFAC World Congress (Submitted), Preprint: ArXiv:2211.05639.

Horuz, C. C., Karlbauer, M., Praditia, T., Butz, M. V., Oladyshkin, S., Nowak, W., & Otte, S. (n.d.). Inferring Boundary Conditions in Finite Volume Neural Networks. International Conference on Artificial Neural Networks 2022.

Zimmermann, N. E. R., Guevara-Carrion, G., Vrabec, J., & Hansen, N. (2022). Predicting and Rationalizing the Soret Coefficient of Binary Lennard-Jones Mixtures in the Liquid State. Advanced Theory and Simulations, 5(11), 2200311. https://doi.org/10.1002/adts.202200311

Zhou, Q., Fehr, J., Bestle, D., & Rui, X. (2022). Simulation of generally shaped 3D elastic body dynamics with large motion using transfer matrix method incorporating model order reduction. Multibody System Dynamics. https://doi.org/10.1007/s11044-022-09869-2

Zhang, X., Divinski, S. V., & Grabowski, B. (2022). Ab initio prediction of vacancy energetics in HCP Al-Hf-Sc-Ti-Zr high entropy alloys and the subsystems. Acta Materialia, 227, 117677. https://doi.org/10.1016/j.actamat.2022.117677

Zaverkin, V., Holzmüller, D., Schuldt, R., & Kästner, J. (2022). Predicting properties of periodic systems from cluster data: A case study of liquid water. The Journal of Chemical Physics, 156(11), 114103. https://doi.org/10.1063/5.0078983

Zaverkin, V., Netz, J., Zills, F., Köhn, A., & Kästner, J. (2022). Thermally Averaged Magnetic Anisotropy Tensors via Machine Learning Based on Gaussian Moments. J. Chem. Theory Comput., 18, 1–12. https://doi.org/10.1021/acs.jctc.1c00853

Zaverkin, V., Holzmüller, D., Steinwart, I., & Kästner, J. (2022). Exploring chemical and conformational spaces by batch mode deep active learning. Digital Discovery, 1, 605–620. https://doi.org/10.1039/D₂DD00034B

Zaverkin, V., Molpeceres, G., & Kästner, J. (2022). Neural-network assisted study of nitrogen atom dynamics on amorphous solid water – II. Diffusion. Mon. Not. R. Astron. Soc., 510(2), 3063–3070. https://doi.org/10.1093/mnras/stab3631

Yi, T., Chu, X., Wang, B., Wu, J., & Yang, G. (2022). Numerical simulation of single bubble evolution in low gravity with fluctuation. International Communications in Heat and Mass Transfer, 130, 105828. https://doi.org/10.1016/j.icheatmasstransfer.2021.105828

Xiao, S., & Nowak, W. (2022). Reliability sensitivity analysis based on a two-stage Markov chain Monte Carlo simulation. Aerospace Science and Technology, 130, 107938. https://doi.org/10.1016/j.ast.2022.107938

Wenzel, T., Santin, G., & Haasdonk, B. (2022). Stability of convergence rates: Kernel interpolation on non-Lipschitz domains. arXiv. https://doi.org/10.48550/ARXIV.2203.12532

Wenzel, T., Kurz, M., Beck, A., Santin, G., & Haasdonk, B. (2022). Structured Deep Kernel Networks for Data-Driven Closure Terms of Turbulent Flows. In I. Lirkov & S. Margenov (Eds.), Large-Scale Scientific Computing (pp. 410--418). Springer International Publishing.

Wenzel, T., Santin, G., & Haasdonk, B. (2022). Analysis of Target Data-Dependent Greedy Kernel Algorithms: Convergence Rates for f-, \$\$f \backslashcdot P\$\$- and f/P-Greedy. Constructive Approximation. https://doi.org/10.1007/s00365-022-09592-3

Weder, B., Barzen, J., Beisel, M., & Leymann, F. (2022). Analysis and Rewrite of Quantum Workflows: Improving the Execution of Hybrid Quantum Algorithms. Proceedings of the 12th International Conference on Cloud Computing and Services Science (CLOSER 2022), 38--50. https://doi.org/10.5220/0011035100003200

Weder, B., Barzen, J., Leymann, F., & Vietz, D. (2022). Quantum Software Development Lifecycle. In M. A. Serrano, R. Pérez-Castillo, & M. Piattini (Eds.), Quantum Software Engineering (pp. 61--83). Springer International Publishing. https://doi.org/10.1007/978-3-031-05324-5_4

Wagner, V., Höpfl, S., Klingel, V., Pop, M. C., & Radde, N. E. (2022). An inverse transformation algorithm to infer parameter distributions from population snapshot data. IFAC-PapersOnLine, 55(23), 86–91. https://doi.org/10.1016/j.ifacol.2023.01.020

Wagner, V., Castellaz, B., Oesting, M., & Radde, N. (2022). Quasi-Entropy Closure : a fast and reliable approach to close the moment equations of the Chemical Master Equation. Bioinformatics, 38(18), 4352–4359. https://doi.org/10.1093/bioinformatics/btac501

Vera, J., Lai, X., Baur, A., Erdmann, M., Gupta, S., Guttà, C., Heinzerling, L., Heppt, M. V., Kazmierczak, P. M., Kunz, M., Lischer, C., Pützer, B. M., Rehm, M., Ostalecki, C., Retzlaff, J., Witt, S., Wolkenhauer, O., & Berking, C. (2022). Melanoma 2.0. Skin cancer as a paradigm for emerging diagnostic technologies, computational modelling and artificial intelligence. Briefings in Bioinformatics. https://doi.org/10.1093/BIB/BBAC433

van Gunsteren, W. F., Pechlaner, M., Smith, L. J., Stankiewicz, B., & Hansen, N. (2022). A Method to Derive Structural Information on Molecules from Residual Dipolar Coupling NMR Data. The Journal of Physical Chemistry B, 126(21), 3867--3888. https://doi.org/10.1021/acs.jpcb.2c02410

Vaikuntanathan, V., Ibach, M., Arad, A., Chu, X., Katoshevski, D., Greenberg, J. B., & Weigand, B. (2022). An Analytical Study on the Mechanism of Grouping of Droplets. Fluids, 7(5), Article 5. https://doi.org/10.3390/fluids7050172

Takamoto, M., Praditia, T., Leiteritz, R., MacKinlay, D., Alesiani, F., Pflüger, D., & Niepert, M. (2022). PDEBench: An Extensive Benchmark for Scientific Machine Learning. 36th Conference on Neural Information Processing Systems (NeurIPS 2022) Track on Datasets and Benchmarks.

Shuva, S., Buchfink, P., Röhrle, O., & Haasdonk, B. (2022). Reduced Basis Methods for Efficient Simulation of a Rigid Robot Hand Interacting with Soft Tissue. In I. Lirkov & S. Margenov (Eds.), Large-Scale Scientific Computing (pp. 402--409). Springer International Publishing.

Seetha, N., & Hassanizadeh, S. M. (2022). A two-way coupled model for the co-transport of two different colloids in porous media. Journal of Contaminant Hydrology, 244, 103922. https://doi.org/10.1016/j.jconhyd.2021.103922

Schäfer Rodrigues Silva, A., Weber, T. K., Gayler, S., Guthke, A., Höge, M., Streck, T., & Nowak, W. (2022). Diagnosing Similarities in Probabilistic Multi-Model Ensembles - an Application to Soil-Plant-Growth-Modeling. Modeling Earth Systems and Environment, 8, 5143–5175. https://doi.org/10.1007/s40808-022-01427-1

Schwarz, A., Kopper, P., Keim, J., Sommerfeld, H., Koch, C., & Beck, A. (2022). A neural network based framework to model particle rebound and fracture. Wear, 508–509, 204476. https://doi.org/10.1016/j.wear.2022.204476

Schmid, P., Ebel, H., & Eberhard, P. (2022). Dependable Data-based Design of Embedded Model Predictive Control. 2022 European Control Conference (ECC), 859–866. https://doi.org/10.23919/ECC55457.2022.9838194

Scherer, C. W. (2022). Dissipativity, Convexity and Tight O\textquotesingleShea-Zames-Falb Multipliers for Safety Guarantees. IFAC-PapersOnLine, 55(30), 150--155. https://doi.org/10.1016/j.ifacol.2022.11.044

Scherer, C. (2022). Dissipativity and Integral Quadratic Constraints, Tailored computational robustness tests for complex interconnections. IEEE Control Systems Magazine (to Appear). https://arxiv.org/abs/2105.07401

Rösinger, C. A., & Scherer, C. W. (2022). Gain-Scheduling Controller Synthesis for Networked Systems with Full Block Scalings.

Rosin, D., Kässinger, J., Yu, X., Avci, O., Sedlmair, M., Rothermel, K., & Röhrle, O. (2022). Neural Network-based Real-time Visualization on Mobile Devices for Pervasive Continuum-Mechanical Simulations in Biomechanics. Medical Image Analysis.

Rosin, D., Kässinger, J., Yu, X., Avci, O., & Röhrle, O. (2022). Neural Network-based Real-time Visualization on Mobile Devices for Pervasive Continuum-Mechanical Simulations in Biomechanics. To Be Submitted to: Medical Image Analysis.

Rosenfelder, M., Ebel, H., Krauspenhaar, J., & Eberhard, P. (2022). Model Predictive Control of Non-Holonomic Vehicles: Beyond Differential-Drive. https://arxiv.org/abs/2205.11400

Rosenfelder, M., Ebel, H., & Eberhard, P. (2022). A Force-Based Control Approach for the Non-Prehensile Cooperative Transportation of Objects Using Omnidirectional Mobile Robots. 2022 IEEE Conference on Control Technology and Applications (CCTA), 349–356. https://doi.org/10.1109/CCTA49430.2022.9966052

Rosenfelder, M., Ebel, H., & Eberhard, P. (2022). Cooperative distributed nonlinear model predictive control of a formation of differentially-driven mobile robots. Robotics and Autonomous Systems, 150(April), 103993. https://doi.org/10.1016/j.robot.2021.103993

Rodriguez-Pretelin, A., Morales-Casique, E., & Nowak, W. (2022). Optimization-based clustering of random fields for computationally efficient and goal-oriented uncertainty quantification: concept and demonstration for delineation of wellhead protection areas in transient aquifers. Advances in Water Resources, 162, 104146. https://doi.org/10.1016/j.advwatres.2022.104146

Rettberg, J., Wittwar, D., Buchfink, P., Brauchler, A., Ziegler, P., Fehr, J., & Haasdonk, B. (2022). Port-Hamiltonian Fluid-Structure Interaction Modeling and Structure-Preserving Model Order Reduction of a Classical Guitar. https://doi.org/10.48550/arXiv.2203.10061

Praditia, T., Karlbauer, M., Otte, S., Oladyshkin, S., Butz, M. V., & Nowak, W. (2022). Learning Groundwater Contaminant Diffusion-Sorption Processes with a Finite Volume Neural Network. Water Resources Research, 58(12), Article 12. https://doi.org/10.1029/2022WR033149

Potyka, J., Stober, J., Wurst, J., Ibach, M., Steigerwald, J., Weigand, B., & Schulte, K. (2022). Towards DNS of Droplet-Jet Collisions of Immiscible Liquids with FS3D. In W. E. Nagel, D. H. Kröner, & M. M. Resch (Eds.), High Performance Computing in Science and Engineering ’22. Springer International Publishing. https://arxiv.org/abs/2212.09727

Pluhackova, K., Schittny, V., Bürkner, P.-C., Siligan, C., & Horner, A. (2022). Multiple pore lining residues modulate water permeability of GlpF. Protein Science, 31(10), e4431. https://doi.org/10.1002/pro.4431

Pechlaner, M., van Gunsteren, W. F., Hansen, N., & Smith, L. J. (2022). Molecular dynamics simulation or structure refinement of proteins: are solvent molecules required? A case study using hen lysozyme. European Biophysics Journal, 51(3), 265--282. https://doi.org/10.1007/s00249-022-01593-1

Oppold, S., & Herschel, M. (2022). Provenance-based explanations: are they useful? International Workshop on the Theory and Practice  of Provenance (TAPP), 2:1--2:4. https://doi.org/10.1145/3530800.3534529

Oladyshkin, S., Praditia, T., Kröker, I., Mohammadi, F., Nowak, W., & Otte, S. (2022). The Deep Arbitrary Polynomial Chaos Neural Network or how Deep Artificial Neural Networks could benefit from Data-Driven Homogeneous Chaos Theory. Neural Networks.

Oesting, M., & Strokorb, K. (2022). A comparative tour through the simulation algorithms for max-stable processes. Statistical Science.

Novikov, I., Grabowski, B., Körmann, F., & Shapeev, A. (2022). Magnetic Moment Tensor Potentials for collinear spin-polarized materials reproduce different magnetic states of bcc Fe. Npj Computational Materials, 8(1), 13. https://doi.org/10.1038/s41524-022-00696-9

Nicodemus, J., Kneifl, J., Fehr, J., & Unger, B. (2022). Physics-informed Neural Networks-based Model Predictive Control for Multi-link Manipulators. IFAC-PapersOnLine, 55(20), 331--336. https://doi.org/10.1016/j.ifacol.2022.09.117

Müller, D., Feilhauer, J., Wickert, J., Berberich, J., Allgöwer, F., & Sawodny, O. (2022). Data-driven predictive disturbance observer for quasi continuum manipulators. Proc. 61st IEEE Conf. Decision and Control (CDC), 1816–1822. https://doi.org/10.1109/CDC51059.2022.9992740

Munz, T., Väth, D., Kuznecov, P., Vu, N. T., & Weiskopf, D. (2022). Visualization-based improvement of neural machine translation. Computers & Graphics, 103, 45–60. https://doi.org/10.1016/j.cag.2021.12.003

Mossier, P., Beck, A., & Munz, C.-D. (2022). A p-adaptive discontinuous Galerkin method with hp-shock capturing. Joural of Scientific Computing, 91(4), Article 4. https://doi.org/10.1007/s10915-022-01770-6

Morales Oreamuno, M. F., Oladyshkin, S., & Nowak, W. (2022). Information-Theoretic Scores for Bayesian Model Selection and Similarity Analysis: Concept and Application to a Groundwater Problem. Water Resources Research. https://doi.org/10.1002/essoar.10512501.1

Molpeceres, G., Kästner, J., Herrero, V. J., Peláez, R. J., & Maté, B. (2022). Desorption of organic molecules from interstellar ices, combining experiments and computer simulations: Acetaldehyde as a case study. Astron. Astrophys., 664, A169. https://doi.org/10.1051/0004-6361/202243489

Molpeceres, G., Jiménez-Serra, I., Oba, Y., Nguyen, T., Watanabe, N., de la Concepción, J. Garc\’ıa, Maté, B., Oliveira, R., & Kästner, J. (2022). Hydrogen abstraction reactions in formic and thioformic acid isomers by hydrogen and deuterium atoms. Astron. Astrophys., 663, A41. https://doi.org/10.1051/0004-6361/202243366

Meister, D., Aurzada, F., Lifshits, M. A., & Allgöwer, F. (2022). Analysis of Time- versus Event-Triggered Consensus for a Single-Integrator Multi-Agent System. Proc. 61st IEEE Conf. on Decision and Control (CDC), 441–446. https://doi.org/10.1109/CDC51059.2022.9993301

Martin, T., & Allgöwer, F. (2022). Determining dissipativity for nonlinear systems from noisy data using Taylor polynomial approximation. Proc. American Control Conf. (ACC), 1432–1437.

Martin, T., & Allgöwer, F. (2022). Data-driven system analysis of nonlinear systems using polynomial approximation. In Proc. American Control Conf. (ACC) (Accepted), Preprint:  ArXiv:2108.11298.

Markthaler, D., Kraus, H., & Hansen, N. (2022). Binding free energies for the SAMPL8 CB8 ``Drugs of Abuse’’ challenge from umbrella sampling combined with Hamiltonian replica exchange. Journal of Computer-Aided Molecular Design, 36(1), 1--9. https://doi.org/10.1007/s10822-021-00439-w

Markthaler, D., Fleck, M., Stankiewicz, B., & Hansen, N. (2022). Exploring the Effect of Enhanced Sampling on Protein Stability Prediction. Journal of Chemical Theory and Computation, 18(4), 2569--2583. https://doi.org/10.1021/acs.jctc.1c01012

Maier, B., Göddeke, D., Huber, F., Klotz, T., Röhrle, O., & Schulte, M. (2022). OpenDiHu: An Efficient and Scalable Framework for Biophysical Simulations of the Neuromuscular System.

Maier, B., & Schulte, M. (2022). Mesh generation and multi-scale simulation of a contracting muscle–tendon complex. Journal of Computational Science, 59, 101559. https://doi.org/10.1016/j.jocs.2022.101559

Luo, W., Chen, J., Ebel, H., & Eberhard, P. (2022). Time-Optimal Handover Trajectory Planning for Aerial Manipulators based on Discrete Mechanics and Complementarity Constraints. https://doi.org/10.48550/arXiv.2209.00533

Luo, W., Eschmann, H., & Eberhard, P. (2022). Gaussian Process Regression-augmented Nonlinear Model Predictive Control for Quadrotor Object Grasping. International Conference on Unmanned Aircraft Systems (ICUAS22), Vol. 16, 11–19. https://doi.org/10.1109/ICUAS54217.2022.9836200

Luo, W., Ebel, H., & Eberhard, P. (2022). An LSTM-based Approach to Precise Landing of a UAV on a Moving Platform. International Journal of Mechanical System Dynamics, 00, 1–12.

Ludwig-Słomczyńska, A. H., & Rehm, M. (2022). Mitochondrial genome variations, mitochondrial-nuclear compatibility, and their association with metabolic diseases. Obesity. https://doi.org/10.1002/OBY.23424

Lieb, D. (2022). Advanced neural network architectures for continuum biomechanical simulation surrogates (D. Rosin, Ed.).

Leiteritz, R., Buchfink, P., Haasdonk, B., & Pflüger, D. (2022). Surrogate-data-enriched Physics-Aware Neural Networks. Proceedings of the Northern Lights Deep Learning Workshop 2022, 3. https://doi.org/10.7557/18.6268

Legrand, J., Naveau, P., & Oesting, M. (2022). Evaluation of binary classifiers for asymptotically dependent and independent extremes.

Lee, J. S., Ko, W.-S., & Grabowski, B. (2022). Atomistic simulations of the deformation behavior of an Nb nanowire embedded in a NiTi shape memory alloy. Acta Materialia, 228, 117764. https://doi.org/10.1016/j.actamat.2022.117764

Köhler, M., Berberich, J., Müller, M. A., & Allgöwer, F. (2022). Data-driven distributed MPC of dynamically coupled linear systems. Proc. 25th Int. Symp. Mathematical Theory of Networks and Systems, 906–911.

Kurz, M., Offenhäuser, P., Viola, D., Shcherbakov, O., Resch, M., & Beck, A. (2022). Deep reinforcement learning for computational fluid dynamics on HPC systems. Journal of Computational Science, 65, 101884. https://doi.org/10.1016/j.jocs.2022.101884

Kurz, M., Offenhäuser, P., Viola, D., Resch, M., & Beck, A. (2022). Relexi — A scalable open source reinforcement learning framework for high-performance computing. Software Impacts, 14, 100422. https://doi.org/10.1016/j.simpa.2022.100422

Kröker, I., Oladyshkin, S., & Rybak, I. (2022). Global sensitivity analysis using multi-resolution polynomial chaos expansion for coupled Stokes-Darcy flow problems. https://doi.org/10.21203/rs.3.rs-1742793/v1

Kröker, I., & Oladyshkin, S. (2022). Arbitrary Multi-Resolution Multi-Wavelet-based Polynomial Chaos Expansion for Data-Driven Uncertainty Quantification. Reliability Engineering & System Safety, 222, 108376. https://doi.org/10.1016/j.ress.2022.108376

Korn, V., & Pluhackova, K. (2022). Not sorcery after all: Roles of multiple charged residues in membrane insertion of gasdermin-A3. Frontiers in Cell and Developmental Biology, 10. https://doi.org/10.3389/fcell.2022.958957

Klink, M. (2022). Time Error Estimators and Adaptive Time-stepping Schemes [Bathesis].

Klingel, V., Graf, D., Weirich, S., Jeltsch, A., & Radde, N. E. (2022). Model-Based Design of a Synthetic Oscillator Based on an Epigenetic Methylation Memory System. ACS Synthetic Biology, 11(7), 2445--2455. https://doi.org/10.1021/acssynbio.2c00118

Kharitenko, A., & Scherer, C. W. (2022). On the exactness of a stability test for Lur’e systems with slope-restricted nonlinearities.

Kessler, C., Schuldt, R., Emmerling, S., Lotsch, B. V., Kästner, J., Gross, J., & Hansen, N. (2022). Influence of layer slipping on adsorption of light gases in covalent organic frameworks: A combined experimental and computational study. Microporous and Mesoporous Materials, 336, 111796. https://doi.org/10.1016/j.micromeso.2022.111796

Kempf, D., Gao, M., Beck, A., Blind, M., Kopper, P., Kuhn, T., Kurz, M., Schwarz, A., & Munz, C.-D. (2022). Development of Turbulent Inflow Methods for the High Order HPC Framework FLEXI. High Performance Computing in Science and Engineering ’21.

Karlbauer, M., Praditia, T., Otte, S., Oladyshkin, S., Nowak, W., & Butz, M. V. (2022). Composing Partial Differential Equations with Physics-Aware Neural Networks. Proceedings of the 39th International Conference on Machine Learning.

Ibach, M., Vaikuntanathan, V., Arad, A., Katoshevski, D., Greenberg, J. B., Schulte, K., & Weigand, B. (2022). Numerical Investigation of Multiple Droplet Streams and the Effect on Grouping Behavior. ILASS-Europe 2022, 31th Conference on Liquid Atomization and Spray Systems, 6-8 September 2022, Tel-Aviv (Virtual).

Ibach, M., Vaikuntanathan, V., Arad, A., Katoshevski, D., Greenberg, J. B., & Weigand, B. (2022). Investigation of droplet grouping in monodisperse streams by direct numerical simulations. Physics of Fluids, 34(8), 083314. https://doi.org/10.1063/5.0097551

Hsueh, H., Guthke, A., Wöhling, T., & Nowak, W. (2022). Diagnosis of model-structural errors with a sliding time-window Bayesian analysis. Water Resources Research, 58, e2021WR030590. https://doi.org/doi:10.1029/2021WR030590

Hornischer, N. (2022). Model Order Reduction with Transformed Modes for Electrophysiological Simulations [Bathesis].

Holzmüller, D., Zaverkin, V., Kästner, J., & Steinwart, I. (2022). A Framework and Benchmark for Deep Batch Active Learning for Regression. ArXiv:1112.5745.

Holicki, T., & Scherer, C. W. (2022). Input-Output-Data-Enhanced Robust Analysis via Lifting.

Holicki, T., & Scherer, C. W. (2022). IQC Based Analysis and Estimator Design for Discrete-Time Systems Affected by Impulsive Uncertainties.

Holicki, T., & Scherer, C. W. (2022). A Dynamic S-Procedure for Dynamic Uncertainties. IFAC-PapersOnline, 55(25), 103–108. https://doi.org/10.1016/j.ifacol.2022.09.331

Hillebrecht, B., & Unger, B. (2022). Certified machine learning: Rigorous a posteriori error bounds for PDE defined PINNs.

Hillebrecht, B., & Unger, B. (2022). Certified machine learning: A posteriori error estimation for physics-informed neural networks. ArXiv E-Print 2203.17055. http://arxiv.org/abs/2203.17055

Hertneck, M., & Allgöwer, F. (2022). Dynamic self-triggered control for nonlinear systems with delays. Proc. 9th IFAC Conf. on Networked Systems (NECSYS), 312–317. https://doi.org/10.1016/j.ifacol.2022.07.278

Hermann, S., & Fehr, J. (2022). Documenting research software in engineering science. Scientific Reports, 12(1), 6567. https://doi.org/10.1038/s41598-022-10376-9

Hagenlocher, C., Siebert, R., Taschke, B., Wieske, S., Hausser, A., & Rehm, M. (2022). ER stress-induced cell death proceeds independently of the TRAIL-R2 signaling axis in pancreatic β cells. Cell Death Discovery, 8(1), 34. https://doi.org/10.1038/s41420-022-00830-y

Gössweiner-Mohr, N., Siligan, C., Pluhackova, K., Umlandt, L., Koefler, S., Trajkovska, N., & Horner, A. (2022). The Hidden Intricacies of Aquaporins: Remarkable Details in a Common Structural Scaffold. Small, 18(31), 2202056. https://doi.org/10.1002/smll.202202056

Guttà, C., Morhard, C., & Rehm, M. (2022). Applying GAN-based data augmentation to improve transcriptome-based prognostication in breast cancer. In medRxiv. Cold Spring Harbor Laboratory Press. https://doi.org/10.1101/2022.10.07.22280776

Guttà, C., Morhard, C., & Rehm, M. (2022). T-GAN-D: a GAN-based classifier for breast cancer                    prognostication. Zenodo. https://doi.org/10.5281/zenodo.7151831

Gramlich, D., Scherer, C. W., & Ebenbauer, C. (2022). Robust Differential Dynamic Programming. 61st IEEE Conf. Decision and Control. https://doi.org/10.1109/cdc51059.2022.9992569

Gramlich, D., Ebenbauer, C., & Scherer, C. W. (2022). Synthesis of Accelerated Gradient Algorithms for Optimization and Saddle Point Problems using Lyapunov functions. Syst. Control Lett., 165. https://arxiv.org/abs/2006.09946

Graf, D., Laistner, L., Klingel, V., Radde, N. E., Weirich, S., & Jeltsch, A. (2022). Reversible switching and stability of the epigenetic memory system in bacteria. The FEBS Journal. https://doi.org/10.1111/febs.16690

Gebhardt, P., Yu, X., Köhn, A., & Sedlmair, M. (2022). MolecuSense: Using Force-Feedback Gloves for Creating and Interacting  with Ball-and-Stick Molecules in VR. http://arxiv.org/abs/2203.09577

Gavrilenko, P., Haasdonk, B., Iliev, O., Ohlberger, M., Schindler, F., Toktaliev, P., Wenzel, T., & Youssef, M. (2022). A Full Order, Reduced Order and Machine Learning Model Pipeline for Efficient Prediction of Reactive Flows. In I. Lirkov & S. Margenov (Eds.), Large-Scale Scientific Computing (pp. 378--386). Springer International Publishing.

Frank, D., Latif, D. A., Muehlebach, M., Unger, B., & Staab, S. (2022). Robust Recurrent Neural Network to Identify Ship Motion in Open Water with Performance Guarantees [Publication]. https://doi.org/10.48550/arXiv.2212.05781

Frank, D., Latif, D. A., Muehlebach, M., & Staab, S. (2022). Robust Recurrent Neural Network to Identify Ship Motion in Open Water with Performance Guarantees -- Technical Report. https://doi.org/10.48550/arXiv.2212.05781

Fiedler, C., Scherer, C. W., & Trimpe, S. (2022, December). Learning Functions and Uncertainty Sets Using Geometrically Constrained Kernel Regression. 61st IEEE Conf. Decision and Control. https://doi.org/10.1109/cdc51059.2022.9993144

Fernández, M., Fritzen, F., & Weeger, O. (2022). Material modeling for parametric, anisotropic finite strain hyperelasticity based on machine learning with application in optimization of metamaterials. International Journal for Numerical Methods in Engineering, 123(2), 577–609. https://doi.org/10.1002/nme.6869

Fellmeth, T. P. (2022). - Live or let die - Bcl-2 protein transmembrane domain interactions in apoptosis signaling. University of Stuttgart.

Eschmann, H., Ebel, H., & Eberhard, P. (2022). Exploration-Exploitation-Based Trajectory Tracking of Mobile Robots Using Gaussian Processes and Model Predictive Control.

Eschmann, H., Ebel, H., & Eberhard, P. (2022). High Accuracy Data-Based Trajectory Tracking of a Mobile Robot. Advances in Service and Industrial Robotics, 31, 420–427. https://doi.org/10.1007/978-3-031-04870-8_49

Eirich, L., Münch, M., Jäckle, D., Sedlmair, M., Bonart, J., & Schreck, T. (2022). RfX: A Design Study for the Interactive Exploration of a Random Forest to Enhance Testing Procedures for Electrical Engines. Computer Graphics Forum (CGF), 14. https://doi.org/10.1111/cgf.14452

Ebel, H., Fahse, D. N., Rosenfelder, M., & Eberhard, P. (2022). Finding Formations for the Non-prehensile Object Transportation with Differentially-Driven Mobile Robots. CISM International Centre for Mechanical Sciences Book Series, 606, 163–170. https://doi.org/10.1007/978-3-031-06409-8_17

Ebel, H., & Eberhard, P. (2022). Cooperative transportation: realizing the promises of robotic networks using a tailored software/hardware architecture. At - Automatisierungstechnik, 70(4), 378–388. https://doi.org/doi:10.1515/auto-2021-0105

de Botton, E., Greenberg, J. B., Arad, A., Katoshevski, D., Vaikuntanathan, V., Ibach, M., & Weigand, B. (2022). An investigation of grouping of two falling dissimilar droplets using the homotopy analysis method. Applied Mathematical Modelling, 104, 486–498. https://doi.org/10.1016/j.apm.2021.12.001

Cheng, K., Lu, Z., Xiao, S., Oladyshkin, S., & Nowak, W. (2022). Mixed covariance function Kriging model for uncertainty quantification. International Journal for Uncertainty Quantification, 12(3), 17--30. https://doi.org/10.1615/Int.J.UncertaintyQuantification.2021035851

Bürkner, P.-C., Kröker, I., Oladyshkin, S., & Nowak, W. (2022). The sparse Polynomial Chaos expansion: a fully Bayesian approach with joint priors on the coefficients and global selection of terms. https://doi.org/10.48550/arXiv.2204.06043

Buchfink, P., Glas, S., & Haasdonk, B. (2022). Optimal Bases for Symplectic Model Order Reduction of Canonizable Linear Hamiltonian Systems. IFAC-PapersOnLine, 55(20), 463--468. https://doi.org/10.1016/j.ifacol.2022.09.138

Breiten, T., Hinsen, D., & Unger, B. (2022). Towards a modeling class for port-Hamiltonian systems with time-delay. https://doi.org/10.48550/arXiv.2211.10687

Breiten, T., & Unger, B. (2022). Passivity preserving model reduction via spectral factorization. Automatica, 142, 110368. https://doi.org/10.1016/j.automatica.2022.110368

Boumaiza, L., Chesnaux, R., Walter, J., Lenhard, R. J., Hassanizadeh, S. M., Dokou, Z., & Alazaiza, M. Y. (2022). Predicting vertical LNAPL distribution in the subsurface under the fluctuating water table effect. Groundwater Monitoring & Remediation. https://doi.org/10.1111/gwmr.12497

Boccellato, C., & Rehm, M. (2022). Glioblastoma, from disease understanding towards optimal cell-based in vitro models. Cellular Oncology. https://doi.org/10.1007/s13402-022-00684-7

Berberich, J., Scherer, C. W., & Allgower, F. (2022). Combining Prior Knowledge and Data for Robust Controller Design. IEEE Transactions on Automatic Control, 1--16. https://doi.org/10.1109/tac.2022.3209342

Berberich, J., Köhler, J., Müller, M. A., & Allgöwer, F. (2022). Stability in data-driven MPC: an inherent robustness perspective. Proc. 61st IEEE Conf. Decision and Control (CDC), 1105–1110. https://doi.org/10.1109/CDC51059.2022.9993361

Berberich, J., Köhler, J., Müller, M. A., & Allgöwer, F. (2022). Linear tracking MPC for nonlinear systems part II: the data-driven case. IEEE Trans. Automat. Control, 67(9), 4406–4421. https://doi.org/10.1109/TAC.2022.3166851

Berberich, J., Köhler, J., Müller, M. A., & Allgöwer, F. (2022). Linear tracking MPC for nonlinear systems part I: the model-based case. IEEE Trans. Automat. Control, 67(9), 4390–4405. https://doi.org/10.1109/TAC.2022.3166872

Banerjee, I., Walter, P., Guthke, A., Mumford, K. G., & Nowak, W. (2022). The Method of Forced Probabilities: A Computation Trick for Bayesian Model Evidence. Computational Geosciences. https://doi.org/10.1007/s10596-022-10179-x

Baier, A., & Staab, S. (2022). A Simulated 4-DOF Ship Motion Dataset for System Identification under Environmental Disturbances. https://doi.org/10.18419/darus-2905

Aseyednezhad, S., Yan, L., Hassanizadeh, M., Raoof, A., & others. (2022). An accurate reduced-dimension numerical model for evolution of electrical potential and ionic concentration distributions in a nano-scale thin aqueous film. Advances in Water Resources, 159, 1--9. https://doi.org/10.1016/j.advwatres.2021.104058

Aseyednezhad, S., Yan, L., Hassanizadeh, S. M., & Raoof, A. (2022). An accurate reduced-dimension numerical model for evolution of electrical potential and ionic concentration distributions in a nano-scale thin aqueous film. Advances in Water Resources, 159, 104058. https://doi.org/10.1016/j.advwatres.2021.104058

Arad, A., Vaikuntanathan, V., Ibach, M., Greenberg, J. B., Weigand, B., & Katoshevski, D. (2022). CFD Simulations of Droplet Grouping in Acoustic Standing Waves. ILASS-Europe 2022, 31th Conference on Liquid Atomization and Spray Systems, 6-8 September 2022, Tel-Aviv (Virtual).

Adam, S., Bräcker, J., Klingel, V., Osteresch, B., Radde, N. E., Brockmeyer, J., Bashtrykov, P., & Jeltsch, A. (2022). Flanking sequences influence the activity of TET1 and TET2 methylcytosine dioxygenases and affect genomic 5hmC patterns. Communications Biology, 5(1), 92. https://doi.org/10.1038/s42003-022-03033-4

Oesting, M., & Strokorb, K. (n.d.). A comparative tour through the simulation algorithms for max-stable processes. Statistical Science.

Zhuang, L., Hassanizadeh, S. M., Bhatt, D., & van Duijn, C. (2021). Spontaneous Imbibition and Drainage of Water in a Thin Porous Layer: Experiments and Modeling. Transport in Porous Media, 139(2), 381--396. https://doi.org/10.1007/s11242-021-01670-7

Zeman, J., Kondrat, S., & Holm, C. (2021). Ionic screening in bulk and under confinement. The Journal of Chemical Physics, 155(20), 204501. https://doi.org/10.1063/5.0069340

Zeifang, J., & Beck, A. (2021). A data-driven high order sub-cell artificial viscosity for the discontinuous Galerkin spectral element method. Journal of Computational Physics, 441, 110475. https://doi.org/10.1016/j.jcp.2021.110475

Zaverkin, V., & Kästner, J. (2021). Exploration of transferable and uniformly accurate neural network interatomic potentials using optimal experimental design. Machine Learning: Science and Technology, 2(3), 035009.

Zaverkin, V., Holzmüller, D., Steinwart, I., & Kästner, J. (2021). Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments. J. Chem. Theory Comput., 17(10), 6658–6670. https://doi.org/10.1021/acs.jctc.1c00527

Zaverkin, V., Holzmüller, D., Steinwart, I., & Kästner, J. (2021). Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments. Journal of Chemical Theory and Computation, 17(10), 6658–6670. https://doi.org/10.1021/acs.jctc.1c00527

Zaverkin, V., Molpeceres, G., & Kästner, J. (2021). Neural-network assisted study of nitrogen atom dynamics on amorphous solid water – II. Diffusion. Mon. Not. R. Astron. Soc., 510(2), 3063–3070. https://doi.org/10.1093/mnras/stab3631

Zaverkin, V., & Kästner, J. (2021). Exploration of transferable and uniformly accurate neural network interatomic potentials using optimal experimental design. Mach. Learn.: Sci. Technol., 2, 035009. https://doi.org/10.1088/2632-2153/abe294

Yu, S., Hirche, M., Huang, Y., Chen, H., & Allgöwer, F. (2021). Model predictive control for autonomous ground vehicles: a review. Auton. Intell. Syst., 1, 4. https://doi.org/10.1007/s43684-021-00005-z

Yiotis, A., Karadimitriou, N., Zarikos, I., & Steeb, H. (2021). Pore-scale effects during the transition from capillary-to viscosity-dominated flow dynamics within microfluidic porous-like domains. Scientific Reports, 11(1), 1--16 (3891). https://doi.org/10.1038/s41598-021-83065-8

Yang, G., Liu, J., Cheng, X., Wang, Y., Chu, X., Mukherjee, S., Terzis, A., Schneemann, A., Li, W., Wu, J., & others. (2021). A superhydrophilic metal--organic framework thin film for enhancing capillary-driven boiling heat transfer. Journal of Materials Chemistry A, 9(45), 25480--25487. https://doi.org/10.1039/D1TA06826A

Xu, X., Binkele, P., Verestek, W., & Schmauder, S. (2021). Molecular Dynamics Simulation of High-Temperature Creep Behavior of Nickel Polycrystalline Nanopillars. Molecules, 26(9), 2606. https://doi.org/10.3390/molecules26092606

Xiao, S., Praditia, T., Oladyshkin, S., & Nowak, W. (2021). Global sensitivity analysis of a CaO/Ca(OH)2 thermochemical energy storage model for parametric effect analysis. Applied Energy, 285, 116456.

Xiao, S., Xu, T., Reuschen, S., Nowak, W., & Franssen, H.-J. H. (2021). Bayesian inversion of multi-Gaussian log-conductivity fields with uncertain hyperparameters: an extension of preconditioned Crank-Nicolson Markov chain Monte Carlo with parallel tempering. Water Resources Research, 57, e2021WR030313. https://doi.org/10.1029/2021WR030313

Xiao, S., Praditia, T., Oladyshkin, S., & Nowak, W. (2021). Global sensitivity analysis of a CaO/Ca(OH)2 thermochemical energy storage model for parametric effect analysis. Applied Energy, 285, 116456. https://doi.org/10.1016/j.apenergy.2021.116456

Wieler, N., Berberich, J., Koch, A., & Allgöwer, F. (2021). Data-driven controller design via finite-horizon dissipativity. Proc. 3rd Learning for Dynamics and Control Conf. (L4DC), 144, 287–298.

Wenzel, T., Kurz, M., Beck, A., Santin, G., & Haasdonk, B. (2021). Structured Deep Kernel Networks for Data-Driven Closure Terms of Turbulent Flows. https://arxiv.org/pdf/2103.13655.pdf

Wenzel, T., Santin, G., & Haasdonk, B. (2021). Analysis of target data-dependent greedy kernel algorithms: Convergence rates for $f$-, $f P$- and $f/P$-greedy. arXiv. https://doi.org/10.48550/ARXIV.2105.07411

Wenzel, T., Santin, G., & Haasdonk, B. (2021). A novel class of stabilized greedy kernel approximation algorithms: Convergence, stability and uniform point distribution. ELSEVIER, 262, 105508. https://doi.org/10.1016/j.jat.2020.105508

Weinhardt, F., Class, H., Vahid Dastjerdi, S., Karadimitriou, N., Lee, D., & Steeb, H. (2021). Experimental Methods and Imaging for Enzymatically Induced Calcite Precipitation in a Microfluidic Cell. Water Resources Research, 57(3), e2020WR029361. https://doi.org/10.1029/2020WR029361

Weder, B., Barzen, J., Leymann, F., & Salm, M. (2021). Automated Quantum Hardware Selection for Quantum Workflows. Electronics, 10(8), Article 8. https://doi.org/10.3390/electronics10080984

Weder, B., Barzen, J., Leymann, F., Salm, M., & Wild, K. (2021). QProv: A provenance system for quantum computing. IET Quantum Communication, 2(4), 171--181. https://doi.org/10.1049/qtc2.12012

Wang, W., Yang, G., Evrim, C., Terzis, A., Helmig, R., & Chu, X. (2021). An assessment of turbulence transportation near regular and random permeable interfaces. Physics of Fluids, 33, 115103. https://doi.org/10.1063/5.0069311

Wang, W., Chu, X., Lozano-Durán, A., Helmig, R., & Weigand, B. (2021). Information transfer between turbulent boundary layers and porous media. Journal of Fluid Mechanics, 920, A21--. https://doi.org/DOI: 10.1017/jfm.2021.445

Wagner, V., Castellaz, B., Oesting, M., & Radde, N. (2021). Quasi-Entropy Closure: A Fast and Reliable Approach to Close the Moment Equations of the Chemical Master Equation.

Wagner, V., & Radde, N. (2021). SiCaSMA: An Alternative Stochastic Description via Concatenation of Markov Processes for a Class of Catalytic Systems. Mathematics, 9, 1074. https://doi.org/10.3390/math9101074

Wagner, A., Eggenweiler, E., Weinhardt, F., Trivedi, Z., Krach, D., Lohrmann, C., Jain, K., Karadimitriou, N., Bringedal, C., Voland, P., & others. (2021). Permeability Estimation of Regular Porous Structures: A Benchmark for Comparison of Methods. Transport in Porous Media, 138(1), 1--23. https://doi.org/10.1007/s11242-021-01586-2

Wagner, A., Eggenweiler, E., Weinhardt, F., Trivedi, Z., Krach, D., Lohrmann, C., Jain, K., Karadimitriou, N., Bringedal, C., Voland, P., Holm, C., Class, H., Steeb, H., & Rybak, I. (2021). Permeability Estimation of Regular Porous Structures: A Benchmark for Comparison of Methods. Transport in Porous Media. https://doi.org/10.1007/s11242-021-01586-2

Veenman, J., Scherer, C. W., Ardura, C., Bennani, S., Preda, V., & Girouart, B. (2021). IQClab: A new IQC based toolbox for robustness analysis and control design. IFAC-PapersOnLine, 54(8), 69--74. https://doi.org/10.1016/j.ifacol.2021.08.583

Tomalka, A., Weidner, S., Hahn, D., Seiberl, W., & Siebert, T. (2021). Power Amplification Increases With Contraction Velocity During Stretch-Shortening Cycles of Skinned Muscle Fibers. Frontiers in Physiology, 12. https://doi.org/10.3389/fphys.2021.644981

Tkachev, G., Frey, S., & Ertl, T. (2021). S4: Self-Supervised learning of Spatiotemporal Similarity. IEEE Transactions on Visualization and Computer Graphics.

Tkachev, G., Frey, S., & Ertl, T. (2021). Local Prediction Models for Spatiotemporal Volume Visualization. IEEE Transactions on Visualization and Computer Graphics, 27(7), 3091–3108. https://doi.org/10.1109/TVCG.2019.2961893

Titze, M., Heitkämper, J., Junge, T., Kästner, J., & Peters, R. (2021). Highly Active Cooperative Lewis Acid—Ammonium Salt Catalyst for the Enantioselective Hydroboration of Ketones. Angew. Chem. Int. Ed., 60(10), 5544–5553. https://doi.org/10.1002/anie.202012796

Szuttor, K., Weik, F., Grad, J.-N., & Holm, C. (2021). Modeling the current modulation of bundled DNA structures in nanopores. The Journal of Chemical Physics, 154(5), 054901. https://doi.org/10.1063/5.0038530

Szuttor, K., Kreissl, P., & Holm, C. (2021). A numerical investigation of analyte size effects in nanopore sensing systems. The Journal of Chemical Physics, 155(13), 134902. https://doi.org/10.1063/5.0065085

Suditsch, M., Schröder, P., Lambers, L., Ricken, T., Ehlers, W., & Wagner, A. (2021). Modelling basal-cell carcinoma behaviour in avascular skin. PAMM, 20(1), Article 1. https://doi.org/10.1002/pamm.202000283

Suditsch, M., Lambers, L., Ricken, T., & Wagner, A. (2021). Application of a continuum-mechanical tumour model to brain tissue. PAMM, 21(1), Article 1. https://doi.org/10.1002/pamm.202100204

Strässer, R., Berberich, J., & Allgöwer, F. (2021). Data-Driven Control of Nonlinear Systems: Beyond Polynomial Dynamics. Proc. 60th IEEE Conf. Decision and Control (CDC), 4344–4351. https://doi.org/10.1109/CDC45484.2021.9683211

Steigerwald, J., Ibach, M., Reutzsch, J., & Weigand, B. (2021). Towards the Numerical Determination of the Splashing Threshold of Two-component Drop Film Interactions. In W. E. Nagel, D. H. Kröner, & M. M. Resch (Eds.), High Performance Computing in Science and Engineering ’20 (pp. 261–279). Springer International Publishing. https://doi.org/10.1007/978-3-030-80602-6_17

Smith, L. J., Gunsteren, W. F., & Hansen, N. (2021). On the Use of Side-Chain NMR Relaxation Data to Derive Structural and Dynamical Information on Proteins: A Case Study Using Hen Lysozyme. ChemBioChem, 22(6), 1049--1064. https://doi.org/10.1002/cbic.202000674

Smith, L. J., van Gunsteren, W. F., Stankiewicz, B., & Hansen, N. (2021). On the use of 3J-coupling NMR data to derive structural information on proteins. Journal of Biomolecular NMR, 75(1), 39--70. https://doi.org/10.1007/s10858-020-00355-5

Shuva, S., Buchfink, P., Röhrle, O., & Haasdonk, B. (2021). Reduced Basis Methods for Efficient Simulation of a Rigid Robot Hand Interacting with Soft Tissue.

Seyedpour, S. M., Nabati, M., Lambers, L., Nafisi, S., Tautenhahn, H.-M., Sack, I., Reichenbach, J. R., & Ricken, T. (2021). Application of Magnetic Resonance Imaging in Liver Biomechanics: A Systematic Review. Frontiers in Physiology, 12. https://doi.org/10.3389/fphys.2021.733393

Seyedpour, S. M., Valizadeh, I., Kirmizakis, P., Doherty, R., & Ricken, T. (2021). Optimization of the Groundwater Remediation Process Using a Coupled Genetic Algorithm-Finite Difference Method. Water, 13(3), 383. https://doi.org/10.3390/w13030383

Scholz, M., & Torkar, R. (2021). An empirical study of Linespots: A novel past-fault algorithm. Software Testing, Verification and Reliability, n/a(n/a), e1787. https://doi.org/10.1002/stvr.1787

Schmalfuss, J., Riethmüller, C., Altenbernd, M., Weishaupt, K., & Göddeke, D. (2021). Partitioned coupling vs. monolithic block-preconditioning approaches for solving Stokes-Darcy systems. Proceedings of the International Conference on Computational Methods for Coupled Problems in Science and Engineering (COUPLED PROBLEMS). https://doi.org/10.23967/coupled.2021.043

Schlor, S., Hertneck, M., Wildhagen, S., & Allgöwer, F. (2021). Multi-party computation enables secure polynomial control based solely on secret-sharing. Proc. 60th IEEE Conf. Decision and Control (CDC), 4882–4887. https://doi.org/10.1109/CDC45484.2021.9683026

Schlaich, A., Jin, D., Bocquet, L., & Coasne, B. (2021). Electronic screening using a virtual Thomas--Fermi fluid for predicting wetting and phase transitions of ionic liquids at metal surfaces. Nature Materials, 1--9. https://doi.org/10.1038/s41563-021-01121-0

Scheurer, S., Schäfer Rodrigues Silva, A., Mohammadi, F., Hommel, J., Oladyshkin, S., Flemisch, B., & Nowak, W. (2021). Surrogate-based Bayesian Comparison of Computationally Expensive Models: Application to Microbially Induced Calcite Precipitation. Computational Geosciences, 25, 1899–1917.

Scherer, C., & Ebenbauer, C. (2021). Convex Synthesis of Accelerated Gradient Algorithms. SIAM Journal on Control and Optimization, 59(6), 4615–4645. https://doi.org/10.1137/21M1398598

Salm, M., Barzen, J., Leymann, F., Weder, B., & Wild, K. (2021). Automating the Comparison of Quantum Compilers for Quantum Circuits. Proceedings of the 15th Symposium and Summer School on Service-Oriented Computing (SummerSOC 2021), 64–80. https://doi.org/10.1007/978-3-030-87568-8_4

Rörich, A., Werthmann, T. A., Göddeke, D., & Grasedyck, L. (2021). Bayesian inversion for electromyography using low-rank tensor formats. Inverse Problems, 37(5), 055003. https://doi.org/10.1088/1361-6420/abd85a

Rosenfelder, M., Ebel, H., & Eberhard, P. (2021). Cooperative Distributed Model Predictive Formation Control of Non-Holonomic Robotic Agents. Proceedings of the 2021 International Symposium on Multi-Robot and Multi-Agent Systems (MRS), 11–19. https://doi.org/10.1109/MRS50823.2021.9620683

Rohde, C., & Tang, H. (2021). On the stochastic Dullin--Gottwald--Holm equation: global existence and wave-breaking phenomena. Nonlinear Differential Equations and Applications NoDEA, 28(5), 1--34. https://doi.org/10.1007/s00030-020-00661-9

Riede, J. M., Holm, C., Schmitt, S., & Haeufle, D. F. B. (2021). The control effort to steer self-propelled microswimmers depends on their morphology: comparing symmetric spherical versus asymmetric              L              -shaped particles. Royal Society Open Science, 8(9), Article 9. https://doi.org/10.1098/rsos.201839

Reuschen, S., Guthke, A., & Nowak, W. (2021). The Four Ways to Consider Measurement Noise in Bayesian Model Selection - And Which One to Choose. Water Resources Research, 57(11), Article 11.

Praditia, T., Karlbauer, M., Otte, S., Oladyshkin, S., Butz, M., & Nowak, W. (2021). Finite Volume Neural Network: Modeling Subsurface Contaminant Transport. Deep Learning for Simulation ICLR Workshop 2021. https://simdl.github.io/files/33.pdf

Pleiss, J. (2021). Standardized data, scalable documentation, sustainable storage –  EnzymeML as a basis for FAIR data management in biocatalysis. ChemCatChem, 13, 3909–3913. https://doi.org/10.1002/cctc.202100822

Osorno, M., Schirwon, M., Kijanski, N., Sivanesapillai, R., Steeb, H., & Göddeke, D. (2021). A cross-platform, high-performance SPH toolkit for image-based flow simulations on the pore scale of porous media. Computer Physics Communications, 267(108059), Article 108059. https://doi.org/10.1016/j.cpc.2021.108059

Orlando, M., Buchholz, P., Lotti, M., & Pleiss, J. (2021). The GH19 Engineering Database: sequence diversity, substrate scope, and evolution in glycoside hydrolase family 19. PLoS One, 16, e0256817. https://doi.org/10.1371/journal.pone.0256817

Oberer, L., Carral, A. D., & Fyta, M. (2021). Simple Classification of RNA Sequences of Respiratory-Related Coronaviruses. ACS Omega. https://doi.org/10.1021/acsomega.1c01625

Murugan, S., Klostermann, S. V., Frey, W., Kästner, J., & Buchmeiser, M. R. (2021). A sodium bis(perfluoropinacol) borate-based electrolyte for stable, high-performance room temperature sodium-sulfur batteries based on sulfurized poly(acrylonitrile). Electrochem. Commun., 132, 107137. https://doi.org/10.1016/j.elecom.2021.107137

Munz, T., Väth, D., Kuznecov, P., Vu, N. T., & Weiskopf, D. (2021). NMTVis - Trained Models for our Visual Analytics System. DaRUS. https://doi.org/10.18419/DARUS-1850

Munz, T., Väth, D., Kuznecov, P., Vu, T., & Weiskopf, D. (2021). Visual-Interactive Neural Machine Translation. Graphics Interface 2021. https://openreview.net/forum?id=DQHaCvN9xd

Molpeceres, G., Zaverkin, V., Watanabe, N., & Kästner, J. (2021). Binding energies and sticking coefficients of H2 on crystalline and amorphous CO ice. A&A, 648, A84. https://doi.org/10.1051/0004-6361/202040023

Molpeceres, G., Zaverkin, V., Watanabe, N., & Kästner, J. (2021). Binding energies and sticking coefficients of H₂ on crystalline and amorphous CO ice. Astron. Astrophys., 648, A84. https://doi.org/10.1051/0004-6361/202040023

Molpeceres, G., & Kästner, J. (2021). Computational Study of the Hydrogenation Sequence of the Phosphorous Atom on Interstellar Dust Grains. Astrophys. J., 910, 55. https://doi.org/10.3847/1538-4357/abe38c

Molpeceres, G., Kästner, J., Fedoseev, G., Qasim, D., Schömig, R., Linnartz, H., & Lamberts, T. (2021). Carbon Atom Reactivity with Amorphous Solid Water: H₂O-Catalyzed Formation of H₂CO. J. Phys. Chem. Lett., 12(44), 10854–10860. https://doi.org/10.1021/acs.jpclett.1c02760

Miksch, A. M., Riffelt, A., Oliveira, R., Kästner, J., & Molpeceres, G. (2021). Hydrogenation of small aromatic heterocycles at low temperatures. Mon. Not. R. Astron. Soc., 505(3), 3157–3164. https://doi.org/10.1093/mnras/stab1514

Miksch, A. M., Morawietz, T., Kästner, J., Urban, A., & Artrith, N. (2021). Strategies for the construction of machine-learning potentials for accurate and efficient atomic-scale simulations. Mach. Learn.: Sci. Technol., 2, 031001. https://doi.org/10.1088/2632-2153/abfd96

Michalowsky, S., Scherer, C., & Ebenbauer, C. (2021). Robust and structure exploiting optimisation algorithms: An integral quadratic constraint approach. International Journal of Control, 94(11), 2956–2979. https://doi.org/10.1080/00207179.2020.1745286

Martin, T., & Allgöwer, F. (2021). Data-driven system analysis of nonlinear systems using polynomial approximation. Preprint:  ArXiv:2108.11298,.

Martin, T., & Allgöwer, F. (2021). Data-driven inference on optimal input-output properties of polynomial systems with focus on nonlinearity measures. IEEE Trans. Automat. Control (Early Access). https://doi.org/10.1109/TAC.2022.3226652

Martin, T., & Allgöwer, F. (2021). Dissipativity Verification With Guarantees for Polynomial Systems From Noisy Input-State Data. IEEE Control Systems Letters, 5(4), 1399–1404. https://doi.org/10.1109/LCSYS.2020.3037842

Markthaler, D., & Hansen, N. (2021). Umbrella sampling and double decoupling data for methanol binding to Candida antarctica lipase B. Data in Brief, 39, 107618. https://doi.org/10.1016/j.dib.2021.107618

Luo, W., Ebel, H., & Eberhard, P. (2021). An LSTM-based Approach to Precise Landing of a UAV on a Moving Platform. International Journal of Mechanical System Dynamics, 00, 1–12.

Liu, Y., Geppert, A., Chu, X., Heine, B., & Weigand, B. (2021). Simulation of an annular liquid jet with a coaxial supersonic gas jet in a medical inhaler. Atomization and Sprays, 31(9), 95–116. https://doi.org/10.1615/AtomizSpr.2021037223

Leiteritz, R., Buchfink, P., Haasdonk, B., & Pflüger, D. (2021). Surrogate-data-enriched Physics-Aware Neural Networks.

Leiteritz, R., Hurler, M., & Pflüger, D. (2021). Learning Free-Surface Flow with Physics-Informed Neural Networks. 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA), 1664–1669. https://doi.org/10.1109/ICMLA52953.2021.00266

Lambers, L., Suditsch, M., Wagner, A., & Ricken, T. (2021). A Multiscale and Multiphase Model of Function-Perfusion Growth Processes in the Human Liver. PAMM, 20(1), Article 1. https://doi.org/10.1002/pamm.202000290

Lambers, L., Mielke, A., & Ricken, T. (2021). Semi-automated Data-driven FE Mesh Generation and Inverse Parameter Identification for a Multiscale and Multiphase Model of Function-Perfusion Processes in the Liver. PAMM, 21(1), Article 1. https://doi.org/10.1002/pamm.202100190

Kühnert, J., Göddeke, D., & Herschel, M. (2021). Provenance-integrated parameter selection and optimization in numerical simulations. International Workshop on the Theory and Practice of Provenance (TAPP).

Képes, K., Leymann, F., Weder, B., & Wild, K. (2021). SiDD: The Situation-Aware Distributed Deployment System. In H. Hacid, F. Outay, H. Paik, A. Alloum, M. Petrocchi, M. R. Bouadjenek, A. Beheshti, X. Liu, & A. Maaradji (Eds.), Service-Oriented Computing  -- ICSOC 2020 Workshops (pp. 72--76). Springer International Publishing.

Kuron, M., Stewart, C., de Graaf, J., & Holm, C. (2021). An extensible lattice Boltzmann method for viscoelastic flows: complex and moving boundaries in Oldroyd-B fluids. https://doi.org/10.1140/epje/s10189-020-00005-6

Krishna Moorthy, N., Seifert, O., Eisler, S., Weirich, S., Kontermann, R. E., Rehm, M., & Fullstone, G. (2021). Low-Level Endothelial TRAIL-Receptor Expression Obstructs the CNS-Delivery of Angiopep-2 Functionalised TRAIL-Receptor Agonists for the Treatment of Glioblastoma. Molecules 2021, Vol. 26, Page 7582, 26(24), 7582. https://doi.org/10.3390/MOLECULES26247582

Konangi, S., Palakurthi, N. K., Karadimitriou, N. K., Comer, K., & Ghia, U. (2021). Comparison of pore-scale capillary pressure to macroscale capillary pressure using direct numerical simulations of drainage under dynamic and quasi-static conditions. Advances in Water Resources, 147, 103792. https://doi.org/10.1016/j.advwatres.2020.103792

Koch, T., Gläser, D., Weishaupt, K., Ackermann, S., Beck, M., Becker, B., Burbulla, S., Class, H., Coltman, E., Emmert, S., & others. (2021). DuMux 3--an open-source simulator for solving flow and transport problems in porous media with a focus on model coupling. Computers & Mathematics with Applications, 81, 423--443. https://doi.org/10.1016/j.camwa.2020.02.012

Kneifl, J., & Fehr, J. (2021). Machine Learning Algorithms for Learning Nonlinear Terms of Reduced Mechanical Models in Explicit Structural Dynamics. PAMM, 20(S1), Article S1. https://doi.org/10.1002/pamm.202000353

Kneifl, J., Grunert, D., & Fehr, J. (2021). A non-intrusive nonlinear model reduction method for structural dynamical problems based on machine learning. International Journal for Numerical Methods in Engineering. https://doi.org/10.1002/nme.6712

Klingel, V., Kirch, J., Ullrich, T., Weirich, S., Jeltsch, A., & Radde, N. E. (2021). Model-based robustness and bistability analysis for methylation-based, epigenetic memory systems. The FEBS Journal, 288(19), 5692–5707. https://doi.org/10.1111/febs.15838

Kempter, F., Kleinbach, C., Staudenmeyer, M., & Fehr, J. (2021). An Active Female Human Body Model for Simulation of Rear-End Impact Scenarios. Proceedings in Applied Mathemathics and Mechanics. https://doi.org/10.1002/pamm.202000068

Karlbauer, M., Praditia, T., Otte, S., Oladyshkin, S., Nowak, W., & Butz, M. V. (2021). Composing Partial Differential Equations with Physics-Aware Neural Networks. Submitted to International Conference on Learning Representations.

Ibach, M., Schulte, K., Vaikuntanathan, V., Arad, A., Katoshevski, D., Greenberg, J. B., & Weigand, B. (2021). Direct Numerical Simulations of Grouping Effects in Droplet Streams Using Different Boundary Conditions. ICLASS 2021, 15th Triennial International Conference on Liquid Atomization and Spray Systems, Edinburgh, UK, 29 Aug.-2 Sept. 2021. https://doi.org/10.2218/iclass.2021.5815

Ibach, M., Schulte, K., Vaikuntanathan, V., Arad, A., Katoshevski, D., Greenberg, B., & Weigand, B. (2021). Direct Numerical Simulations of Grouping Effects in Droplet Streams Using Different Boundary Conditions. International Conference on Liquid Atomization and Spray Systems (ICLASS), 1(1), Article 1. https://doi.org/10.2218/iclass.2021.5815

Hsueh, H., Guthke, A., Wöhling, T., & Nowak, W. (2021). Diagnosis of model-structural errors with a sliding time-window Bayesian analysis. Submitted to Water Resources Research.

Holzmüller, D., & Pflüger, D. (2021). Fast Sparse Grid Operations Using the Unidirectional Principle: A Generalized and Unified Framework. In H.-J. Bungartz, J. Garcke, & D. Pflüger (Eds.), Sparse Grids and Applications - Munich 2018 (pp. 69--100). Springer International Publishing.

Holzmüller, D. (2021). On the Universality of the Double Descent Peak in Ridgeless Regression. International Conference on Learning Representations. https://openreview.net/forum?id=0IO5VdnSAaH

Holicki, T., & Scherer, C. W. (2021). Revisiting and Generalizing the Dual Iteration for Static and Robust Output-Feedback Synthesis. Int. J. Robust Nonlin., 1–33. https://doi.org/10.1002/rnc.5547

Holicki, T., & Scherer, C. W. (2021). Algorithm Design and Extremum Control:Convex Synthesis due to Plant Multiplier Commutation. 60th IEEE Conf. Decision and Control.

Holicki, T., Scherer, C. W., & Trimpe, S. (2021). Controller Design via Experimental Exploration with Robustness Guarantees. IEEE Control Syst. Lett., 5(2), 641–646. https://doi.org/10.1109/LCSYS.2020.3004506

Holicki, T., & Scherer, C. W. (2021). Robust Gain-Scheduled Estimation with Dynamic D-Scalings. IEEE Trans. Autom. Control, 66(11), 5592–5598. https://doi.org/10.1109/TAC.2021.3052751

Hitz, T., Jöns, S., Heinen, M., Vrabec, J., & Munz, C.-D. (2021). Comparison of macro-and microscopic solutions of the Riemann problem II. Two-phase shock tube. Journal of Computational Physics, 429, 110027. https://doi.org/10.1016/j.jcp.2020.110027

Hilder, B. (2021). Modulating traveling fronts in a dispersive Swift-Hohenberg equation coupled to an additional conservation law.

Hertneck, M., & Allgöwer, F. (2021). Dynamic self-triggered control for nonlinear systems based on hybrid Lyapunov functions. Proc. 60th IEEE Conf. Decision and Control (CDC), 533–539. https://doi.org/10.1109/CDC45484.2021.9682784

Hertneck, M., Linsenmayer, S., & Allgöwer, F. (2021). Efficient stability analysis approaches for nonlinear  weakly-hard real-time control systems. Automatica, 133, 109868. https://doi.org/10.1016/j.automatica.2021.109868

Herkert, R. (2021). Model Order Reduction and Geometry Parametrization for Linear Elasticity.

Heinemann, M., Frey, S., Tkachev, G., Straub, A., Sadlo, F., & Ertl, T. (2021). Visual analysis of droplet dynamics in large-scale multiphase spray simulations. Journal of Visualization, 24(5), 943--961. https://doi.org/10.1007/s12650-021-00750-6

Haasdonk, B., Ohlberger, M., & Schindler, F. (2021). An adaptive model hierarchy for data-augmented training of kernel models for reactive flow.

Haasdonk, B., Wenzel, T., Santin, G., & Schmitt, S. (2021). Biomechanical surrogate modelling using stabilized vectorial greedy kernel methods. In Numerical Mathematics and Advanced Applications ENUMATH 2019 (pp. 499--508). Springer. https://doi.org/10.1007/978-3-030-55874-1_49

Göhring, M. (2021). Model Order Reduction with Kernel Autoencoders.

Grioui, F., & Blascheck, T. (2021). Study of Heart Rate Visualizations on a Virtual Smartwatch. https://doi.org/10.1145/3489849.3489913

Gramlich, D., Ebenbauer, C., & Scherer, C. W. (2021). Convex Synthesis of Accelerated Gradient Algorithms for Optimization and Saddle Point Problems using Lyapunov functions. Accepted for Syst. Control Lett. https://arxiv.org/abs/2006.09946

Gosea, I. V., Gugercin, S., & Unger, B. (2021). Parametric model reduction via rational interpolation along parameters. ArXiv E-Print 2104.01016. https://arxiv.org/abs/2104.01016

Garcia, R., Munz, T., & Weiskopf, D. (2021). Visual analytics tool for the interpretation of hidden states in recurrent neural networks. Visual Computing for Industry, Biomedicine, and Art, 4(24), Article 24. https://doi.org/10.1186/s42492-021-00090-0

Gao, H., Tatomir, A. B., Karadimitriou, N. K., Steeb, H., & Sauter, M. (2021). A two-phase, pore-scale reactive transport model for the kinetic interface-sensitive tracer. Water Resources Research, 57(6), e2020WR028572. https://doi.org/10.1029/2020WR028572

Gao, H., Tatomir, A., Karadimitriou, N., Steeb, H., & Sauter, M. (2021). Effects of surface roughness on the kinetic interface-sensitive tracer transport during drainage processes. Advances in Water Resources, 157, 104044. https://doi.org/10.1016/j.advwatres.2021.104044

Gao, B., Coltman, E., Farnsworth, J., Helmig, R., & Smits, K. M. (2021). Determination of Vapor and Momentum Roughness Lengths Above an Undulating Soil Surface Based on PIV-Measured Velocity Profiles. Water Resources Research, 57(7), e2021WR029578. https://doi.org/10.1029/2021WR029578

Flaig, S., Praditia, T., Kissinger, A., Lang, U., Oladyshkin, S., & Nowak, W. (2021). Prognosis of water levels in a moor groundwater system influenced by hydrology and water extraction using an artificial neural network. EGU General Assembly 2021.

Fiedler, C., Scherer, C. W., & Trimpe, S. (2021). Learning-enhanced robust controller synthesis with rigorous statistical and control-theoretic guarantees. 60th IEEE Conf. Decision and Control.

Fiedler, C., Scherer, C. W., & Trimpe, S. (2021). Practical and Rigorous Uncertainty Bounds for Gaussian Process Regression. Proceedings of the AAAI Conference on Artificial Intelligence, 35(8), 7439–7447.

Fernández, M., Fritzen, F., & Weeger, O. (2021). Material modeling for parametric, anisotropic finite strain hyperelasticity based on machine learning with application in optimization of metamaterials. International Journal for Numerical Methods in Engineering, n/a(n/a), 33. https://doi.org/10.1002/nme.6869

Fehr, J., Himpe, C., Rave, S., & Saak, J. (2021). Sustainable Research Software Hand-Over. Journal of Open Research Software, 9(5), Article 5. https://doi.org/10.5334/jors.307

Evrim, C., Chu, X., Silber, F. E., Isaev, A., Weihe, S., & Laurien, E. (2021). Flow features and thermal stress evaluation in turbulent mixing flows. 178, 121605. https://doi.org/10.1016/j.ijheatmasstransfer.2021.121605

Eschmann, H. (2021). A Data Set for Research on Data-based Methods for an Omnidirectional Mobile Robot. DaRUS. https://doi.org/10.18419/DARUS-1845

Eschmann, H., Ebel, H., & Eberhard, P. (2021). Data-Based Model of an Omnidirectional Mobile Robot Using Gaussian Processes. IFAC Symposium on System Identification (SYSID) - Learning Models for Decision and Control, 13–18. https://doi.org/10.1016/j.ifacol.2021.08.327

Eschmann, H., & Eberhard, P. (2021). Learning-Based Model Predictive Control for Multi-Agent Systems using Gaussian Processes. PAMM, 20(1), e202000009. https://doi.org/10.1002/pamm.202000009

Eschmann, H., Ebel, H., & Eberhard, P. (2021). Trajectory tracking of an omnidirectional mobile robot using Gaussian process regression. At - Automatisierungstechnik, 69(8), 656--666. https://doi.org/doi:10.1515/auto-2021-0019

Emmerling, S. T., Schuldt, R., Bette, S., Yao, L., Dinnebier, R. E., Kästner, J., & Lotsch, B. V. (2021). Interlayer Interactions as Design Tool for Large-Pore COFs. J. Am. Chem. Soc., 143(38), 15711–15722. https://doi.org/10.1021/jacs.1c06518

Ehring, T. (2021). Feedback control for dynamic soft tissue systems by a surrogate of the value function.

Ebel, H., & Eberhard, P. (2021). Non-Prehensile Cooperative Object Transportation with Omnidirectional Mobile Robots: Organization, Control, Simulation, and Experimentation. 2021 International Symposium on Multi-Robot and Multi-Agent Systems (MRS), 1–10. https://doi.org/10.1109/MRS50823.2021.9620541

Dürrwächter, J., Kurz, M., Kopper, P., Kempf, D., Munz, C.-D., & Beck, A. (2021). An efficient sliding mesh interface method for high-order discontinuous Galerkin schemes. Computers & Fluids, 217, 104825. https://doi.org/10.1016/j.compfluid.2020.104825

Dingler, C., Müller, H., Wieland, M., Fauser, D., Steeb, H., & Ludwigs, S. (2021). Actuators: From Understanding Mechanical Behavior to Curvature Prediction of Humidity-Triggered Bilayer Actuators (Adv. Mater. 9/2021). Advanced Materials, 33(9), 2170067. https://doi.org/10.1002/adma.202170067

Diestelkämper, R., Lee, S., Herschel, M., & Glavic, B. (2021). To not miss the forest for the trees - A holistic approach for explaining missing answers over nested data. In Proceedins of the ACM SIG Conference on the Management of Data (SIGMOD).

de Winter, D., Weishaupt, K., Scheller, S., Frey, S., Raoof, A., Hassanizadeh, S., & Helmig, R. (2021). The complexity of porous media flow characterized in a microfluidic model based on confocal laser scanning microscopy and micro-piv. Transport in Porous Media, 136(1), 343--367. https://doi.org/10.1007/s11242-020-01515-9

Chu, X., Wang, W., Müller, J., Schöning, H. V., Liu, Y., & Weigand, B. (2021). Turbulence Modulation and Energy Transfer in Turbulent Channel Flow Coupled with One-Side Porous Media. In High Performance Computing in Science and Engineering’20 (pp. 373--386). Springer. https://doi.org/10.1007/978-3-030-80602-6_24

Chu, X., Wang, W., Yang, G., Terzis, A., Helmig, R., & Weigand, B. (2021). Transport of turbulence across permeable interface in a turbulent channel flow: interface-resolved direct numerical simulation. Transport in Porous Media, 136(1), 165--189. https://doi.org/10.1007/s11242-020-01506-w

Christ, B., Collatz, M., Dahmen, U., Herrmann, K.-H., Höpfl, S., König, M., Lambers, L., Marz, M., Meyer, D., Radde, N., Reichenbach, J. R., Ricken, T., & Tautenhahn, H.-M. (2021). Hepatectomy-Induced Alterations in Hepatic Perfusion and Function - Toward Multi-Scale Computational Modeling for a Better Prediction of Post-hepatectomy Liver Function. Frontiers in Physiology, 12. https://doi.org/10.3389/fphys.2021.733868

Cheng, K., Lu, Z., Xiao, S., Oladyshkin, S., & Nowak, W. (2021). Unified Bayesian inference framework for surrogate modelling: connection between existing techniques and their common fundamentals. Submitted to Reliability Engineering and System Safety.

Cheng, K., Z, L., Xiao, S., Oladyshkin, S., & Nowak, W. (2021). Mixed covariance function Kriging model for uncertainty quantification. Submitted to International Journal for Uncertainty Quantification.

Cheng, K., Xiao, S., Zhang, X., Oladyshkin, S., & Nowak, W. (2021). Resampling method for reliability-based design optimization based on thermodynamic integration and parallel tempering. Mechanical Systems and Signal Processing, 156, 107630. https://doi.org/10.1016/j.ymssp.2021.107630

Chen, Y., Steeb, H., Erfani, H., Karadimitriou, N. K., Walczak, M. S., Ruf, M., Lee, D., An, S., Hasan, S., Connolley, T., & others. (2021). Nonuniqueness of hydrodynamic dispersion revealed using fast 4D synchrotron x-ray imaging. Science Advances, 7(52), eabj0960. https://doi.org/10.1126/sciadv.abj0960

Chalapco, A. (2021). Markov Chain Monte Carlo for Artificial Neural Networks. BSc Thesis, University of Stuttgart.

Chalapco, A. (2021). Uncertainty Quantification in Neural Networks. Project Work Report, University of Stuttgart.

Carvalho, H., Ferrario, V., & Pleiss, J. (2021). The molecular mechanism of methanol inhibition in CALB-catalyzed alcoholysis: analyzing molecular dynamics simulations by a Markov state model. J Chem Theory Comput, 17, 6570–6582. https://doi.org/10.1021/acs.jctc.1c00559

Carral, Á. D., Ostertag, M., & Fyta, M. (2021). Deep learning for nanopore ionic current blockades. The Journal of Chemical Physics, 154(4), 044111. https://doi.org/10.1063/5.0037938

Buchfink, P., Glas, S., & Haasdonk, B. (2021). Symplectic Model Reduction of Hamiltonian Systems on Nonlinear Manifolds. https://doi.org/10.48550/arXiv.2112.10815

Buchfink, P., & Haasdonk, B. (2021). Experimental Comparison of Symplectic and Non-symplectic Model Order Reduction an Uncertainty Quantification Problem. In F. J. Vermolen & C. Vuik (Eds.), Numerical Mathematics and Advanced Applications ENUMATH 2019 (Vol. 139). Springer International Publishing. https://doi.org/10.1007/978-3-030-55874-1

Brunn, M., Himthani, N., Biros, G., Mehl, M., & Mang, A. (2021). Fast GPU 3D diffeomorphic image registration. Journal of Parallel and Distributed Computing, 149, 149--162. https://doi.org/10.1016/j.jpdc.2020.11.006

Breiten, T., & Unger, B. (2021). Passivity preserving model reduction via spectral factorization. ArXiv E-Print 2103.13194. https://arxiv.org/abs/2103.13194

Born, D., & Kästner, J. (2021). Geometry Optimization in Internal Coordinates Based on Gaussian Process Regression: Comparison of Two Approaches. J. Chem. Theory Comput., 17(9), 5955–5967. https://doi.org/10.1021/acs.jctc.1c00517

Boccellato, C., Kolbe, E., Peters, N., Juric, V., Fullstone, G., Verreault, M., Idbaih, A., Lamfers, M. L. M., Murphy, B. M., & Rehm, M. (2021). Marizomib sensitizes primary glioma cells to apoptosis induced by a latest-generation TRAIL receptor agonist. Cell Death & Disease, 12(7), 647. https://doi.org/10.1038/s41419-021-03927-x

Bertrand, F., Lambers, L., & Ricken, T. (2021). Least Squares Finite Element Method for Hepatic Sinusoidal Blood Flow. PAMM, 20(1), Article 1. https://doi.org/10.1002/pamm.202000306

Berberich, J., Köhler, J., Müller, M. A., & Allgöwer, F. (2021). On the design of terminal ingredients for data-driven MPC. Proc. 7th IFAC Conf. Nonlinear Model Predictive Control (NMPC), 257–263. https://doi.org/10.1016/j.ifacol.2021.08.554

Berberich, J., Wildhagen, S., Hertneck, M., & Allgöwer, F. (2021). Data-driven analysis and control of continuous-time systems under aperiodic sampling. Proc. 19th IFAC Symp. System Identification (SYSID), 210–215. https://doi.org/10.1016/j.ifacol.2021.08.360

Berberich, J., Köhler, J., Müller, M. A., & Allgöwer, F. (2021). Data-driven model predictive control: closed-loop guarantees and experimental results. At-Automatisierungstechnik, 69(7), 608–618. https://doi.org/10.1515/auto-2021-0024

Benacchio, T., Bonaventura, L., Altenbernd, M., Cantwell, C. D., Düben, P. D., Gillard, M., Giraud, L., Göddeke, D., Raffin, E., Teranishi, K., & Wedi, N. (2021). Resilience and fault tolerance in high-performance computing for numerical weather and climate prediction. The International Journal of High Performance Computing Applications, 35(4), 285–311. https://doi.org/10.1177/1094342021990433

Beck, A., Gao, M., Kempf, D., Kopper, P., Krais, N., Kurz, M., Zeifang, J., & Munz, C.-D. (2021). Increasing the flexibility of the high order discontinuous Galerkin framework FLEXI towards large scale industrial applications. In W. E. Nagel, D. H. Kröner, & M. M. Resch (Eds.), High Performance Computing in Science and Engineering ’20. Springer International Publishing.

Beck, A., & Kurz, M. (2021). A perspective on machine learning methods in turbulence modeling. GAMM-Mitteilungen, 44(1), Article 1. https://doi.org/10.1002/gamm.202100002

Banerjee, I., Guthke, A., Van de Ven, C. J. C., Mumford, K. G., & Nowak, W. (2021). Overcoming the model-data-fit problem in porous media: A quantitative method to compare invasion-percolation models to high-resolution data. Water Resources Research, 57(7), e2021WR029986. https://doi.org/10.1029/2021WR029986

Baier, A., Boukhers, Z., & Staab, S. (2021). Hybrid Physics and Deep Learning Model for Interpretable Vehicle State Prediction. http://arxiv.org/abs/2103.06727

Aslannejad, H., Loginov, S., van der Hoek, B., Schoonderwoerd, E., Gerritsen, H., & Hassanizadeh, S. (2021). Liquid droplet imbibition into a thin coating layer: direct pore-scale modeling and experimental observations. Progress in Organic Coatings, 151, 106054. https://doi.org/10.1016/j.porgcoat.2020.106054

Armiti-Juber, A., & Ricken, T. (2021). Model order reduction for deformable porous materials in thin domains via asymptotic analysis. Archive of Applied Mechanics. https://doi.org/10.1007/s00419-021-01907-3

Arad, A., Katoshevski, D., Vaikuntanathan, V., Ibach, M., Greenberg, J. B., & Weigand, B. (2021, December). Longitudinal and Lateral Grouping in Droplet Streams using the Eulerian-Lagrangian Approach.

Alsalti, M., Berberich, J., Lopez, V. G., Allgöwer, F., & Müller, M. A. (2021). Data-Based System Analysis and Control of Flat Nonlinear Systems. Proc. 60th IEEE Conf. Decision and Control (CDC), 1484–1489. https://doi.org/10.1109/CDC45484.2021.9683327

Alonso-Orán, D., Rohde, C., & Tang, H. (2021). A Local-in-Time Theory for Singular SDEs with Applications to Fluid Models with Transport Noise. Journal of Nonlinear Science. https://doi.org/10.1007/s00332-021-09755-9

Zimmermann, M., Breitenbücher, U., Képes, K., Leymann, F., & Weder, B. (2020). Data Flow Dependent Component Placement of Data Processing Cloud Applications. 2020 IEEE International Conference on Cloud Engineering (IC2E), 83–94. https://doi.org/10.1109/IC2E48712.2020.00016

Zeman, J., Kondrat, S., & Holm, C. (2020). Bulk ionic screening lengths from extremely large-scale molecular dynamics simulations. Chem. Commun., 56(100), 15635–15638. https://doi.org/10.1039/D0CC05023G

Zaverkin, V., & Kästner, J. (2020). Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning Potentials. Journal of Chemical Theory and Computation, 16(8), 5410--5421. https://doi.org/10.1021/acs.jctc.0c00347

Zaverkin, V., & Kästner, J. (2020). Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning Potentials. J. Chem. Theory Comput., 16, 5410–5421. https://doi.org/10.1021/acs.jctc.0c00347

Yu, X., Angerbauer, K., Mohr, P., Kalkofen, D., & Sedlmair, M. (2020). Perspective Matters: Design Implications for Motion Guidance in Mixed Reality. Proceedings of the IEEE 19th International Symposium on Mixed and Augmented Reality.

Yang, G. (杨光), Chu, X. (初旭), Vaikuntanathan, V., Wang, S. (王珊珊), Wu, J. (吴静怡), Weigand, B., & Terzis, A. (2020). Droplet mobilization at the walls of a microfluidic channel. Physics of Fluids, 32(1), 012004. https://doi.org/10.1063/1.5139308

Xu, X., Range, J., Gygli, G., & Pleiss, J. (2020). Analysis of thermophysical properties of deep eutectic solvents by data integration. J Chem Eng Data, 65, 1172–1179. https://doi.org/10.1021/acs.jced.9b00555

Xu, T., Reuschen, S., Nowak, W., & Franssen, H.-J. H. (2020). Preconditioned Crank-Nicolson Markov chain Monte Carlo coupled with parallel tempering: An efficient method for Bayesian inversion of multi-Gaussian log-hydraulic conductivity fields. Water Resources Research, 56(8), e2020WR027110. https://doi.org/10.1029/2020WR027110

Xiao, S., Oladyshkin, S., & Nowak, W. (2020). Reliability analysis with stratified importance sampling based on adaptive Kriging. Reliability Engineering & System Safety, 197, 106852. https://doi.org/10.1016/j.ress.2020.106852

Xiao, S., Oladyshkin, S., & Nowak, W. (2020). Reliability analysis with conditional importance sampling based on adaptive Kriging. Reliability Engineering & System Safety, 197, 106852. https://doi.org/10.1016/j.ress.2020.106852

Xiao, S., Oladyshkin, S., & Nowak, W. (2020). Forward-reverse switch between density-based and regional sensitivity analysis. Applied Mathematical Modelling, 84, 377–392.

Wochner, I., Driess, D., Zimmermann, H., Haeufle, D. F., Toussaint, M., & Schmitt, S. (2020). Optimality principles in human point-to-manifold reaching accounting for muscle dynamics. Frontiers in Computational Neuroscience, 14, 38.

Wild, K., Breitenbücher, U., Képes, K., Leymann, F., & Weder, B. (2020). Decentralized Cross-Organizational Application Deployment Automation: An Approach for Generating Deployment Choreographies Based on Declarative Deployment Models. Proceedings of the 32nd Conference on Advanced Information Systems Engineering (CAiSE 2020), 12127, 20--35. https://doi.org/10.1007/978-3-030-49435-3_2

Weder, B., Breitenbücher, U., Képes, K., Leymann, F., & Zimmermann, M. (2020). Deployable Self-Contained Workflow Models. Proceedings of the 8th European Conference on Service-Oriented and Cloud Computing (ESOCC 2020), 85--96. https://doi.org/10.1007/978-3-030-44769-4_7

Weder, B., Barzen, J., Leymann, F., Salm, M., & Vietz, D. (2020). The Quantum Software Lifecycle. Proceedings of the 1st ACM SIGSOFT International Workshop on Architectures and Paradigms for Engineering Quantum Software (APEQS 2020), 2--9. https://doi.org/10.1145/3412451.3428497

Weder, B., Breitenbücher, U., Leymann, F., & Wild, K. (2020). Integrating Quantum Computing into Workflow Modeling and Execution. 2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC), 279–291. https://doi.org/10.1109/UCC48980.2020.00046

Vetma, V., Guttà, C., Peters, N., Praetorius, C., Hutt, M., Seifert, O., Meier, F., Kontermann, R., Kulms, D., & Rehm, M. (2020). Convergence of pathway analysis and pattern recognition predicts sensitization to latest generation TRAIL therapeutics by IAP antagonism. Cell Death & Differentiation, 27(8), 2417--2432. https://doi.org/10.1038/s41418-020-0512-5

Tovey, S., Krishnamoorthy, A. N., Sivaraman, G., Guo, J., Benmore, C., Heuer, A., & Holm, C. (2020). DFT Accurate Interatomic Potential for Molten NaCl from Machine Learning. The Journal of Physical Chemistry C, 124(47), 25760--25768. https://doi.org/10.1021/acs.jpcc.0c08870

Tomalka, A., Weidner, S., Hahn, D., Seiberl, W., & Siebert, T. (2020). Cross-Bridges and Sarcomeric Non-cross-bridge Structures Contribute to Increased Work in Stretch-Shortening Cycles. Frontiers in Physiology, 11. https://doi.org/10.3389/fphys.2020.00921

Stöhr, D., Schmid, J. O., Beigl, T. B., Mack, A., Maichl, D. S., Cao, K., Budai, B., Fullstone, G., Kontermann, R. E., Mürdter, T. E., Tait, S. W. G., Hagenlocher, C., Pollak, N., Scheurich, P., & Rehm, M. (2020). Stress-induced TRAILR2 expression overcomes TRAIL resistance in cancer cell spheroids. Cell Death & Differentiation, 27, 3037–3052. https://doi.org/10.1038/s41418-020-0559-3

Stöhr, D., Jeltsch, A., & Rehm, M. (2020). TRAIL receptor signaling: From the basics of canonical signal transduction toward its entanglement with ER stress and the unfolded protein response. Cell Death Regulation in Health and Disease-Part A, 57.

Stöhr, D., & Rehm, M. (2020). Linking hyperosmotic stress and apoptotic sensitivity. The FEBS Journal, febs.15520. https://doi.org/10.1111/febs.15520

Stockinger, P., Roth, S., Müller, M., & Pleiss, J. (2020). Systematic evaluation of imine-reducing enzymes: Common principles in imine reductases, β-hydroxyacid dehydrogenases, and short-chain dehydrogenases/reductases. ChemBioChem, 21, 2689–2695.

Steigerwald, J., Ibach, M., Reutzsch, J., & Weigand, B. (2020). Towards the Numerical Determination of the Splashing Threshold of Two-component Drop Film Interactions. In High Performance Computing in Science and Engineering ’20. Springer.

Sood, E., Tannert, S., Frassinelli, D., Bulling, A., & Vu, N. T. (2020). Interpreting Attention Models with Human Visual Attention in Machine Reading Comprehension. Proceedings of the 24th Conference on Computational Natural Language Learning, 12--25. https://doi.org/10.18653/v1/2020.conll-1.2

Sood, E., Tannert, S., Mueller, P., & Bulling, A. (2020). Improving Natural Language Processing Tasks with Human Gaze-Guided Neural Attention. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, & H. Lin (Eds.), Advances in Neural Information Processing Systems (Vol. 33, pp. 6327--6341). Curran Associates, Inc. https://proceedings.neurips.cc/paper/2020/file/460191c72f67e90150a093b4585e7eb4-Paper.pdf

Sivaraman, G., Krishnamoorthy, A. N., Baur, M., Holm, C., Stan, M., Csányi, G., Benmore, C., & Vázquez-Mayagoitia, Á. (2020). Machine-learned interatomic potentials by active learning: amorphous and liquid hafnium dioxide. Npj Computational Materials, 6(1), Article 1. https://doi.org/10.1038/s41524-020-00367-7

Schäfer Rodrigues Silva, A., Guthke, A., Höge, M., Cirpka, O. A., & Nowak, W. (2020). Strategies for simplifying reactive transport models - a Bayesian model comparison. Water Resources Research, 56, e2020WR028100. https://doi.org/10.1029/2020WR028100

Schneider, M., Weishaupt, K., Gläser, D., Boon, W. M., & Helmig, R. (2020). Coupling staggered-grid and MPFA finite volume methods for free flow/porous-medium flow problems. Journal of Computational Physics, 401, 109012. https://doi.org/10.1016/j.jcp.2019.109012

Schlottke, K., Reutzsch, J., Kieffer-Roth, C., & Weigand, B. (2020). Direct Numerical Simulations of Evaporating Droplets at Higher Temperatures: Application of a Consistent Numerical Approach. Droplet Interactions and Spray Processes, 287–299.

Schepp, L. L., Ahrens, B., Balcewicz, M., Duda, M., Nehler, M., Osorno, M., Uribe, D., Steeb, H., Nigon, B., Stöckhert, F., Swanson, D. A., Siegert, M., Gurris, M., Saenger, E. H., & Ruf, M. (2020). Digital rock physics and laboratory considerations on a high-porosity volcanic rock: micro-XRCT data sets. In DaRUS. https://doi.org/10.18419/DARUS-680

Schepp, L. L., Ahrens, B., Balcewicz, M., Duda, M., Nehler, M., Osorno, M., Uribe, D., Steeb, H., Nigon, B., Stöckhert, F., & others. (2020). Digital rock physics and laboratory considerations on a high-porosity volcanic rock. Scientific Reports, 10(1), 1--16.

Salm, M., Barzen, J., Breitenbücher, U., Leymann, F., Weder, B., & Wild, K. (2020). The NISQ Analyzer: Automating the Selection of Quantum Computers for Quantum Algorithms. Proceedings of the 14th Symposium and Summer School on Service-Oriented Computing (SummerSOC 2020), 66--85. https://doi.org/10.1007/978-3-030-64846-6_5

Salm, M., Barzen, J., Leymann, F., & Weder, B. (2020, November). About a Criterion of Successfully Executing a Circuit in the NISQ Era: What $wd 1/\epsilon_eff$ Really Means. Proceedings of the 1st ACM SIGSOFT International Workshop on Architectures and Paradigms for Engineering Quantum Software. https://doi.org/10.1145/3412451.3428498

Rösinger, C. A., & Scherer, C. W. (2020). A Flexible Synthesis Framework of Structured Controllers for Networked Systems. IEEE Trans. Control Netw. Syst., 7(1), 6–18. https://doi.org/10.1109/TCNS.2019.2914411

Rösinger, C. A., & Scherer, C. W. (2020). Lifting to Passivity for $H_2$-Gain-Scheduling Synthesis with Full Block Scalings. IFAC-PapersOnLine, 53(2), 7292–7298. https://doi.org/10.1016/j.ifacol.2020.12.570

Reutzsch, J., Kieffer-Roth, C., & Weigand, B. (2020). A consistent method for direct numerical simulation of droplet evaporation. Journal of Computational Physics, 109455. https://doi.org/10.1016/j.jcp.2020.109455

Praditia, T., Walser, T., Oladyshkin, S., & Nowak, W. (2020). Improving Thermochemical Energy Storage Dynamics Forecast with Physics-Inspired Neural Network Architecture. Energies, 13(15), 3873. https://doi.org/10.3390/en13153873

Oladyshkin, S., Mohammadi, F., Kröker, I., & Nowak, W. (2020). Bayesian3 active learning for Gaussian process emulator using information theory. Entropy, 22(0890), 1–27. https://doi.org/10.3390/e22080890

Oladyshkin, S., Mohammadi, F., Kroeker, I., & Nowak, W. (2020). Bayesian3 Active Learning for the Gaussian Process Emulator Using Information Theory. Entropy, 22(8), 890. https://doi.org/10.3390/e22080890

Nguyen, L. T. K., Rambausek, M., & Keip, M.-A. (2020). Variational framework for distance-minimizing method in data-driven computational mechanics. Computer Methods in Applied Mechanics and Engineering, 365, 112898. https://doi.org/10.1016/j.cma.2020.112898

Müller, S. (2020). Symplectic Neural Networks.

Müller, P., Sood, E., & Bulling, A. (2020). Anticipating Averted Gaze in Dyadic Interactions. ACM Symposium on Eye Tracking Research and Applications, 1–10. https://doi.org/10.1145/3379155.3391332

Munz, T., Schäfer, N., Blascheck, T., Kurzhals, K., Zhang, E., & Weiskopf, D. (2020). Supplemental Material for Comparative Visual Gaze Analysis for Virtual Board Games. DaRUS. https://doi.org/10.18419/DARUS-1130

Munz, T., Schaefer, N., Blascheck, T., Kurzhals, K., Zhang, E., & Weiskopf, D. (2020). Demo of a Visual Gaze Analysis System for Virtual Board Games. ACM Symposium on Eye Tracking Research and Applications. https://doi.org/10.1145/3379157.3391985

Munz, T., Schäfer, N., Blascheck, T., Kurzhals, K., Zhang, E., & Weiskopf, D. (2020). Comparative Visual Gaze Analysis for Virtual Board Games. The 13th International Symposium on Visual Information Communication and Interaction (VINCI 2020). https://doi.org/10.1145/3430036.3430038

Molpeceres, G., Zaverkin, V., & Kästner, J. (2020). Neural-network assisted study of nitrogen atom dynamics on amorphous solid water--I. adsorption and desorption. Monthly Notices of the Royal Astronomical Society, 499(1), 1373--1384.

Molpeceres, G., Zaverkin, V., & Kästner, J. (2020). Neural-network assisted study of nitrogen atom dynamics on amorphous solid water – I. adsorption and desorption. Mon. Not. R. Astron. Soc., 499, 1373–1384. https://doi.org/10.1093/mnras/staa2891

Michalowsky, S., Scherer, C., & Ebenbauer, C. (2020). Robust and structure exploiting optimisation algorithms : an integral quadratic constraint approach. International Journal of Control, 2020, 1–24. https://doi.org/10.1080/00207179.2020.1745286

Michalowsky, S., Scherer, C. W., & Ebenbauer, C. (2020). Robust and structure exploiting optimization algorithms: An integral quadratic constraint approach. Int. J. Control, 1–24. https://doi.org/10.1080/00207179.2020.1745286

Martin, T., Koch, A., & Allgöwer, F. (2020). Data-driven surrogate models for LTI systems via saddle-point dynamics. Proc. 21st IFAC World Congress, 971–976. https://doi.org/10.1016/j.ifacol.2020.12.1261

Martin, T., & Allgöwer, F. (2020). Iterative data-driven inference of nonlinearity measures via successive graph approximation. Proc. 59th IEEE Conf. Decision and Control (CDC), 4760–4765. https://doi.org/10.1109/CDC42340.2020.9304285

Mangiagalli, M., Carvalho, H., Natalello, A., Ferrario, V., Pennati, M., Barbiroli, A., Lotti, M., Pleiss, J., & Brocca, S. (2020). Diverse effects of aqueous polar co-solvents on Candida antarctica lipase B. Int J Biol Macromol, 150, 930–940.

Kurz, M., & Beck, A. (2020). A machine learning framework for LES closure terms. ETNA - Electronic Transactions on Numerical Analysis, 117–137. https://doi.org/10.1553/etna_vol56s117

Kuritz, K., Stöhr, D., Maichl, D. S., Pollak, N., Rehm, M., & Allgöwer, F. (2020). Reconstructing temporal and spatial dynamics from single-cell pseudotime using prior knowledge of real scale cell densities. Scientific Reports, 10(1), 3619. https://doi.org/10.1038/s41598-020-60400-z

Kunc, O., & Fritzen, F. (2020). Many-scale finite strain computational homogenization via Concentric Interpolation. International Journal for Numerical Methods in Engineering, 121(21), 4689--4716. https://doi.org/10.1002/nme.6454

Kneifl, J., Grunert, D., & Fehr, J. (2020). A non-intrusive nonlinear model reduction method for structural dynamical problems based on machine learning. Universität Stuttgart. https://doi.org/10.18419/OPUS-11181

Kempter, F., Bechler, F., & Fehr, J. (2020). Calibration Approach for Muscle Activated Human Models in Pre-Crash Maneuvers with a Driver-in-the-Loop Simulator. Proceedings in 6th Digital Human Modeling Symposium. https://doi.org/10.3233/ATDE200029

Imig, D., Pollak, N., Allgöwer, F., & Rehm, M. (2020). Sample-based modeling reveals bidirectional interplay between cell cycle progression and extrinsic apoptosis. PLOS Computational Biology, 16(6), 1–17. https://doi.org/10.1371/journal.pcbi.1007812

Häufle, D. F. B., Wochner, I., Holzmüller, D., Driess, D., Günther, M., & Schmitt, S. (2020). Muscles Reduce Neuronal Information Load : Quantification of Control Effort in Biological vs. Robotic Pointing and Walking. Frontiers In Robotics and AI, 7, 77. https://doi.org/10.3389/frobt.2020.00077

Häufle, D. F. B., Wochner, I., Holzmüller, D., Drieß, D., Günther, M., & Schmitt, S. (2020). Muscles reduce neuronal information load : quantification of control effort in biological vs. robotic pointing and walking. Frontiers in Robotics and AI, 7, 77. https://doi.org/10.3389/frobt.2020.00077

Holzmüller, D., & Steinwart, I. (2020). Training two-layer ReLU networks with gradient descent is inconsistent. ArXiv:2002.04861. https://arxiv.org/abs/2002.04861

Holzmüller, D. (2020). On the Universality of the Double Descent Peak in Ridgeless Regression. ArXiv Preprint ArXiv:2010.01851. https://openreview.net/pdf?id=0IO5VdnSAaH

Holicki, T., & Scherer, C. W. (2020). Output-Feedback Synthesis for a Class of Aperiodic Impulsive Systems. IFAC-PapersOnline, 53(2), 7299–7304. https://doi.org/10.1016/j.ifacol.2020.12.981

Hirche, M., Köhler, P. N., Müller, M. A., & Allgöwer, F. (2020). Distributed Model Predictive Control for Consensus of Constrained Heterogeneous Linear Systems. Proc. 59th IEEE Conf. on Decision and Control (CDC), 1248–1253. https://doi.org/10.1109/CDC42340.2020.9303838

Heyen, F., Munz, T., Neumann, M., Ortega, D., Vu, N. T., Weiskopf, D., & Sedlmair, M. (2020). ClaVis: An Interactive Visual Comparison System for Classifiers. Proceedings of the International Conference on Advanced Visual Interfaces, 9, 1--9. https://doi.org/10.1145/3399715.3399814

Hertneck, M., Linsenmayer, S., & Allgöwer, F. (2020). Stability Analysis for Nonlinear Weakly Hard Real-Time Control Systems. Proc. 21st IFAC World Congress, 2632–2637. https://doi.org/10.1016/j.ifacol.2020.12.307

Hertneck, M., & Allgöwer, F. (2020). Exploiting Information for Decentralized Periodic Event-Triggered Control. Proc. 59th IEEE Conf. Decision and Control (CDC), 4999–5004. https://doi.org/10.1109/CDC42340.2020.9304456

Hertneck, M., Linsenmayer, S., & Allgöwer, F. (2020). Model-Based Nonlinear Periodic Event-Triggered Control for Continuous-Time Systems with Sampled-Data Prediction. Proc. European Control Conf. (ECC), 1814–1819.

Heck, K., Coltman, E., Schneider, J., & Helmig, R. (2020). Influence of Radiation on Evaporation Rates: A Numerical Analysis. Water Resources Research, 56(10), Article 10. https://doi.org/10.1029/2020wr027332

Hasan, S., Niasar, V., Karadimitriou, N. K., Godinho, J. R., Vo, N. T., An, S., Rabbani, A., & Steeb, H. (2020). Direct characterization of solute transport in unsaturated porous media using fast X-ray synchrotron microtomography. Proceedings of the National Academy of Sciences, 117(38), 23443--23449. https://doi.org/10.1073/pnas.2011716117

Hansen, N., Öehlknecht, C., de Ruiter, A., Lier, B., van Gunsteren, W. F., Oostenbrink, C., & Gebhardt, J. (2020). A Suite of Advanced Tutorials for the GROMOS Biomolecular Simulation Software Article v1.0. Living Journal of Computational Molecular Science, 2(1), Article 1. https://doi.org/10.33011/livecoms.2.1.18552

Gygli, G., & Pleiss, J. (2020). Simulation foundry: Automated and FAIR molecular modeling. Journal of Chemical Information and Modeling, 60(4), 1922--1927.

Gygli, G., Xu, X., & Pleiss, J. (2020). Meta-analysis of viscosity of aqueous deep eutectic solvents and their components. Sci Rep, 10, 21395–21395.

Guttà, C., Rahman, A., Aura, C., Dynoodt, P., Charles, E. M., Hirschenhahn, E., Joseph, J., Wouters, J., de Chaumont, C., Rafferty, M., Warren, M., van den Oord, J. J., Gallagher, W. M., & Rehm, M. (2020). Low expression of pro-apoptotic proteins Bax, Bak and Smac indicates prolonged progression-free survival in chemotherapy-treated metastatic melanoma. Cell Death & Disease, 11(2), Article 2. https://doi.org/10.1038/s41419-020-2309-3

Gebhardt, J., Kiesel, M., Riniker, S., & Hansen, N. (2020). Combining Molecular Dynamics and Machine Learning to Predict Self-Solvation Free Energies and Limiting Activity Coefficients. Journal of Chemical Information and Modeling, 60(11), 5319--5330. https://doi.org/10.1021/acs.jcim.0c00479

Fullstone, G., Guttà, C., Beyer, A., & Rehm, M. (2020). The FLAME-accelerated signalling tool (FaST) for facile parallelisation of flexible agent-based models of cell signalling. Npj Systems Biology and Applications, 6(1), 10--. https://doi.org/10.1038/s41540-020-0128-x

Fullstone, G., Bauer, T. L., Guttà, C., Salvucci, M., Prehn, J. H. M., & Rehm, M. (2020). The apoptosome molecular timer synergises with XIAP to suppress apoptosis execution and contributes to prognosticating survival in colorectal cancer. Cell Death & Differentiation, 27, 2828–2842. https://doi.org/10.1038/s41418-020-0545-9

Flemisch, B., Hermann, S., Holm, C., Mehl, M., Reina, G., Uekermann, B., Boehringer, D., Ertl, T., Grad, J.-N., Iglezakis, D., Jaust, A., Koch, T., Seeland, A., Weeber, R., Weik, F., & Weishaupt, K. (2020). Umgang mit Forschungssoftware an der Universität Stuttgart. Universität Stuttgart. https://doi.org/10.18419/OPUS-11178

Flaig, S. (2020). Prognose von Wasserständen in einem durch die Trinkwassergewinnung beeinflussten Moor-Grundwassersystem mithilfe eines künstlichen neuronalen Netzwerks. MSc Thesis, University of Stuttgart.

Fischer, M., Bauer, G., & Gross, J. (2020). Transferable Anisotropic United-Atom Mie (TAMie) Force Field: Transport Properties from Equilibrium Molecular Dynamic Simulations. Industrial & Engineering Chemistry Research, 59(18), 8855--8869.

Fernández, M., Rezaei, S., Mianroodi, J. R., Fritzen, F., & Reese, S. (2020). Application of artificial neural networks for the prediction of interface mechanics: a study on grain boundary constitutive behavior. Advanced Modeling and Simulation in Engineering Sciences, 7(1), 27. https://doi.org/10.1186/s40323-019-0138-7

Fernández, M., & Fritzen, F. (2020). On the generation of periodic discrete structures with identical two-point correlation. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 476(2242), 20200568. https://doi.org/10.1098/rspa.2020.0568

Fernández, M., & Fritzen, F. (2020). Construction of a Class of Sharp Löwner Majorants for a Set of Symmetric Matrices. Journal of Applied Mathematics, 2020, 18. https://doi.org/10.1155/2020/9091387

Fernández, M., & Fritzen, F. (2020). Construction of a class of sharp Löwner majorants for a set of symmetric matrices. Journal of Applied Mathematics, 2020, 1–18. https://doi.org/10.1155/2020/9091387

Fernandez, M., & Fritzen, F. (2020). On the generation of periodic discrete structures with identical two-point correlation. Proceedings of the Royal Society A, 476(2242), 20200568.

Eisenkolb, I., Jensch, A., Eisenkolb, K., Kramer, A., Buchholz, P., Pleiss, J., Spiess, A., & Radde, N. (2020). Modeling of biocatalytic reactions: A workflow for model calibration, selection and validation using Bayesian statistics. AIChE J, 66, e16866.

Diestelkämper, R., & Herschel, M. (2020). Tracing nested data with structural provenance for big data analytics. Proceedings of the International Conference on Extending Database Technology (EDBT), 253–264. https://doi.org/10.5441/002/edbt.2020.23

Diestelkämper, R., & Herschel, M. (2020). Distributed Tree-Pattern Matching in Big Data Analytics Systems. In Proceedings of the Conference on Advances in Databases and Information Systems (ADBIS), 171–186. https://doi.org/10.1007/978-3-030-54832-2_14

Denzel, A., & Kästner, J. (2020). Hessian Matrix Update Scheme for Transition State Search Based on Gaussian Process Regression. J. Chem. Theory Comput., 16, 5083–5089. https://doi.org/10.1021/acs.jctc.0c00348

de Souza, F. A. L., Sivaraman, G., Fyta, M., Scheicher, R. H., Scopel, W. L., & Amorim, R. G. (2020). Electrically sensing Hachimoji DNA nucleotides through a hybrid graphene/h-BN nanopore. Nanoscale, 12(35), 18289–18295. https://doi.org/10.1039/D0NR04363J

Cooper, A. M., Kästner, J., Urban, A., & Artrith, N. (2020). Efficient training of ANN potentials by including atomic forces via Taylor expansion and application to water and a transition-metal oxide. Npj Computational Materials, 6(1), 1--14.

Coltman, E., Lipp, M., Vescovini, A., & Helmig, R. (2020). Obstacles, Interfacial Forms, and Turbulence: A Numerical Analysis of Soil--Water Evaporation Across Different Interfaces. Transport in Porous Media. https://doi.org/10.1007/s11242-020-01445-6

Chu, X. (初旭), Liu, Y. (刘雁超), Wang, W. (王文康), Yang, G. (杨光), Weigand, B., & Nemati, H. (2020). Turbulence, pseudo-turbulence, and local flow topology in dispersed bubbly flow. Physics of Fluids, 32(8), 083310. https://doi.org/10.1063/5.0014833

Chu, X., Wu, Y., Rist, U., & Weigand, B. (2020). Instability and transition in an elementary porous medium. Phys. Rev. Fluids, 5(4), 044304. https://doi.org/10.1103/PhysRevFluids.5.044304

Buchfink, P., Haasdonk, B., & Rave, S. (2020). PSD-Greedy Basis Generation for Structure-Preserving Model Order Reduction of Hamiltonian Systems. In P. Frolkovič, K. Mikula, & D. Ševčovič (Eds.), Proceedings of the Conference Algoritmy 2020 (pp. 151--160). Vydavateľstvo SPEKTRUM. http://www.iam.fmph.uniba.sk/amuc/ojs/index.php/algoritmy/article/view/1577/829

Brencher, L., & Barth, A. (2020). Hyperbolic Conservation Laws with Stochastic Discontinuous Flux Functions. International Conference on Finite Volumes for Complex Applications, 265--273.

Breitsprecher, K., Janssen, M., Srimuk, P., Mehdi, B. L., Presser, V., Holm, C., & Kondrat, S. (2020). How to speed up ion transport in nanopores. Nature Communications, 11(1), Article 1. https://doi.org/10.1038/s41467-020-19903-6

Berberich, J., Koch, A., Scherer, C. W., & Allgower, F. (2020). Robust data-driven state-feedback design. 2020 American Control Conference (ACC), 1532–1538. https://doi.org/10.23919/acc45564.2020.9147320

Berberich, J., Scherer, C. W., & Allgöwer, F. (2020). Combining Prior Knowledge and Data for Robust Controller Design. https://arxiv.org/abs/2009.05253

Beckers, F., Heredia, A., Noack, M., Nowak, W., Wieprecht, S., & Oladyshkin, S. (2020). Bayesian Calibration and Validation of a Large-scale and Time-demanding Sediment Transport Model. Water Resources Research, 56(7), e2019WR026966. https://doi.org/10.1029/2019WR026966

Beck, A. D., Zeifang, J., Schwarz, A., & Flad, D. G. (2020). A neural network based shock detection and localization approach for discontinuous Galerkin methods. Journal of Computational Physics, 423, 109824. https://doi.org/10.1016/j.jcp.2020.109824

Bauer, T., Buchholz, P., & Pleiss, J. (2020). The modular structure of α/β-hydrolases. FEBS J, 287, 1035–1053.

Barreau, M., Scherer, C. W., Gouaisbaut, F., & Seuret, A. (2020). Integral Quadratic Constraints on Linear Infinite-dimensional Systems for Robust Stability Analysis. IFAC World Congress.

Bahlmann, L. M., Smits, K. M., Heck, K., Coltman, E., Helmig, R., & Neuweiler, I. (2020). Gas Component Transport Across the Soil-Atmosphere Interface for Gases of Different Density: Experiments and Modeling. Water Resources Research, 56(9), e2020WR027600. https://doi.org/10.1029/2020WR027600

Alonso-Orán, D., Rohde, C., & Tang, H. (2020). A local-in-time theory for singular SDEs with applications to fluid models with transport noise. ArXiv Preprint ArXiv:2010.09972, 1–32. https://doi.org/10.1007/s00332-021-09755-9

Alonso-Orán, D., Rohde, C., & Tang, H. (2020). A local-in-time theory for singular SDEs with applications to fluid models with transport noise. ArXiv Preprint ArXiv:2010.09972, 1–32. https://doi.org/10.1007/s00332-021-09755-9

Adam, S., Anteneh, H., Hornisch, M., Wagner, V., Lu, J., Radde, N., Bashtrykov, P., Song, J., & Jeltsch, A. (2020). DNA sequence-dependent activity and base flipping mechanisms of DNMT1 regulate genome-wide DNA methylation. Nat. Communications, 11(1), 1–15. https://doi.org/10.1038/s41467-020-17531-8

Zeman, J., Holm, C., & Smiatek, J. (2019). The Effect of Small Organic Cosolutes on Water Structure and Dynamics. Journal of Chemical & Engineering Data, 65(3), 1197--1210. https://doi.org/10.1021/acs.jced.9b00577

Xu, X., Range, J., Gygli, G., & Pleiss, J. (2019). Analysis of Thermophysical Properties of Deep Eutectic Solvents by Data Integration. Journal of Chemical & Engineering Data, 65(3), 1172--1179. https://doi.org/10.1021/acs.jced.9b00555

Xiao, S., Reuschen, S., Köse, G., Oladyshkin, S., & Nowak, W. (2019). Estimation of small failure probabilities based on thermodynamic integration and parallel tempering. Mechanical Systems and Signal Processing, 133, 106248. https://doi.org/10.1016/j.ymssp.2019.106248

Xiao, S., Reuschen, S., Köse, G., Oladyshkin, S., & Nowak, W. (2019). Estimation of small failure probabilities based on thermodynamic integration and parallel tempering. Mechanical Systems and Signal Processing, 133, 106248.

Tomalka, A., Röhrle, O., Han, J.-C., Pham, T., Taberner, A. J., & Siebert, T. (2019). Extensive eccentric contractions in intact cardiac trabeculae: revealing compelling differences in contractile behaviour compared to skeletal muscles. Proceedings of the Royal Society B, 286(1903), 20190719.

Tkachev, G., Frey, S., & Ertl, T. (2019). Local Prediction Models for Spatiotemporal Volume Visualization. IEEE Transactions on Visualization and Computer Graphics. https://doi.org/10.1109/TVCG.2019.2961893

Terzis, A., Zarikos, I., Weishaupt, K., Yang, G., Chu, X., Helmig, R., & Weigand, B. (2019). Microscopic velocity field measurements inside a regular porous medium adjacent to a low Reynolds number channel flow. Physics of Fluids, 31(4), 042001. https://doi.org/10.1063/1.5092169

Steeb, H., & Renner, J. (2019). Mechanics of Poro-Elastic Media: A Review with Emphasis on Foundational State Variables. Transport in Porous Media, 130(2), 437--461.

Rösinger, C. A., & Scherer, C. W. (2019). A Flexible Synthesis Framework of Structured Controllers for Networked Systems. IEEE Trans. Control Netw. Syst., 7(1), 6–18. https://doi.org/10.1109/TCNS.2019.2914411

Rösinger, C. A., & Scherer, C. W. (2019). A Scalings Approach to $H_2$-Gain-Scheduling Synthesis without Elimination. IFAC-PapersOnLine, 52(28), 50–57. https://doi.org/10.1016/j.ifacol.2019.12.347

Romer, A., Trimpe, S., & Allgöwer, F. (2019). Data-driven inference of passivity properties via Gaussian process optimization. 2019 18th European Control Conference (ECC), 29--35.

Romer, A., Berberich, J., Köhler, J., & Allgöwer, F. (2019). One-shot verification of dissipativity properties from input--output data. IEEE Control Systems Letters, 3(3), 709--714.

Roddan, R., Gygli, G., Sula, A., Mendez-Sanchez, D., Pleiss, J., Ward, J., Keep, N., & Hailes, H. (2019). The acceptance and kinetic resolution of alpha-methyl substituted aldehydes by norcoclaurine synthases. ACS Catal, 9, 9640–9649.

Ricken, T., & Lambers, L. (2019). On computational approaches of liver lobule function and perfusion simulation. GAMM-Mitteilungen, 42(4), Article 4. https://doi.org/10.1002/gamm.201900016

Reutzsch, J., Raja Kochanattu, G. V., Ibach, M., Kieffer-Roth, C., Tonini, S., Cossali, G. E., & Weigand, B. (2019). Direct Numerical Simulations of Oscillating Liquid Droplets: a Method to Extract Shape Characteristics. ILASS-Europe 2019, 29th Conference on Liquid Atomization and Spray Systems, Paris, France.

Oladyshkin, S., & Nowak, W. (2019). The connection between Bayesian Inference and Information Theory for model selection, information gain and experimental design. Entropy, 21, 1081. https://doi.org/doi:10.3390/e21111081

Munz, T., Burch, M., van Benthem, T., Poels, Y., Beck, F., & Weiskopf, D. (2019). Overlap-Free Drawing of Generalized Pythagoras Trees for Hierarchy Visualization. 2019 IEEE Visualization Conference (VIS), 251–255. https://doi.org/10.1109/VISUAL.2019.8933606

Munz, T., Chuang, L. L., Pannasch, S., & Weiskopf, D. (2019). VisME: Visual microsaccades explorer. Journal of Eye Movement Research, 12(6), Article 6. https://doi.org/10.16910/jemr.12.6.5

Martin, T., & Allgöwer, F. (2019). Nonlinearity Measures for Data-Driven System Analysis and Control. Proc. 58th IEEE Conf. Decision and Control (CDC), 3605–3610. https://doi.org/10.1109/CDC40024.2019.9029804

Lambers, L., Ricken, T., & König, M. (2019). A multiscale and multiphase model for the description of function-perfusion processes in the human liver. Advances in Engineering Materials, Structures and Systems : Innovations, Mechanics and Applications : Proceedings of the 7th International Conference on Structural Engineering, Mechanics and Computation (SEMC 2019), September 2-4, 2019, Cape Town, South Africa, 304–307. https://doi.org/10.1201/9780429426506-52

Lambers, L., Ricken, T., & König, M. (2019). Model Order Reduction (MOR) of Function--Perfusion--Growth Simulation in the Human Fatty Liver via Artificial Neural Network (ANN). PAMM, 19(1), Article 1. https://doi.org/10.1002/pamm.201900429

Kuhn, T., Dürrwächter, J., Meyer, F., Beck, A., Rohde, C., & Munz, C.-D. (2019). Uncertainty quantification for direct aeroacoustic simulations of cavity flows. J. Theor. Comput. Acoust., 27(1, 1850044), Article 1, 1850044. https://doi.org/10.1142/S2591728518500445

Holicki, T., & Scherer, C. W. (2019). A Homotopy Approach for Robust Output-Feedback Synthesis. Proc. 27th. Med. Conf. Control Autom., 87–93. https://doi.org/10.1109/MED.2019.8798536

Holicki, T., & Scherer, C. W. (2019). Stability Analysis and Output-Feedback Synthesis of Hybrid Systems Affected by Piecewise Constant Parameters via Dynamic Resetting Scalings. Nonlinear Anal. Hybri., 34, 179–208. https://doi.org/10.1016/j.nahs.2019.06.003

Hertneck, M., Linsenmayer, S., & Allgöwer, F. (2019). Nonlinear Dynamic Periodic Event-Triggered Control with Robustness to Packet Loss Based on Non-Monotonic Lyapunov Functions. Proc. 58th IEEE Conf. Decision and Control (CDC), 1680–1685. https://doi.org/10.1109/CDC40024.2019.9029770

Harzenetter, L., Breitenbücher, U., Leymann, F., Saatkamp, K., Weder, B., & Wurster, M. (2019). Automated Generation of Management Workflows for Applications Based on Deployment Models. 2019 IEEE 23rd International Enterprise Distributed Object Computing Conference (EDOC), 216–225. https://doi.org/10.1109/EDOC.2019.00034

Göküzüm, F. S., Nguyen, L. T. K., & Keip, M.-A. (2019). An Artificial Neural Network Based Solution Scheme for Periodic Computational Homogenization of Electrostatic Problems. Mathematical and Computational Applications, 24(2), 40. https://doi.org/10.3390/mca24020040

Göküzüm, F. S., Nguyen, L. T. K., & Keip, M.-A. (2019). An Artificial Neural Network based Solution Scheme to periodic Homogenization. PAMM, 19(1), Article 1. https://doi.org/10.1002/pamm.201900271

Grabowski, B., Ikeda, Y., Srinivasan, P., Körmann, F., Freysoldt, C., Duff, A. I., Shapeev, A., & Neugebauer, J. (2019). Ab initio vibrational free energies including anharmonicity for multicomponent alloys. Npj Computational Materials, 5(1), 1--6.

Geissen, E.-M., Hasenauer, J., & Radde, N. E. (2019). Inference of finite mixture models and the effect of binning. Statistical Applications in Genetics and Molecular Biology, 18(4), Article 4.

Ferrario, V., Fischer, M., Zhu, Y., & Pleiss, J. (2019). Modelling of substrate access and substrate binding to cephalosporin acylases. Scientific Reports, 9(1), Article 1. https://doi.org/10.1038/s41598-019-48849-z

Driess, D., Schmitt, S., & Toussaint, M. (2019). Active Inverse Model Learning with Error and Reachable Set Estimates. IROS, 1826--1833.

Denzel, A., Haasdonk, B., & Kästner, J. (2019). Gaussian Process Regression for Minimum Energy Path Optimization and Transition State Search. J. Phys. Chem. A, 123(44), 9600–9611. https://doi.org/10.1021/acs.jpca.9b08239

Chu, X., Yang, G., Pandey, S., & Weigand, B. (2019). Direct numerical simulation of convective heat transfer in porous media. International Journal of Heat and Mass Transfer, 133, 11--20. https://doi.org/10.1016/j.ijheatmasstransfer.2018.11.172

Carral, A. D., Sarap, C. S., Liu, K., Radenovic, A., & Fyta, M. (2019). 2D MoS2 nanopores: ionic current blockade height for clustering DNA events. 2D Materials, 6(4), 045011.

Baz, J., Held, C., Pleiss, J., & Hansen, N. (2019). Thermophysical properties of glyceline–water mixtures investigated by molecular modelling. Phys. Chem. Chem. Phys., 21(12), 6467–6476. https://doi.org/10.1039/C9CP00036D

Baggio, G., Zampieri, S., & Scherer, C. W. (2019). Gramian Optimization with Input-Power Constraints. 2019 IEEE 58th Conference on Decision and Control (CDC), 5686–5691. https://doi.org/10.1109/CDC40024.2019.9029169