Publications of EXC 2075

  1. 2021

    1. 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
    2. 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.
    3. 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
    4. 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
    5. 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
    6. 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
    7. 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
    8. 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
    9. 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
    10. 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
    11. 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
    12. 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
    13. 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
    14. 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
    15. 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
    16. 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.
    17. 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
    18. 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
    19. 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
    20. 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.
    21. 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
    22. 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
    23. 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
    24. 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
    25. 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
    26. 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
    27. 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
    28. 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
    29. 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
    30. Breiten, T., & Unger, B. (2021). Passivity preserving model reduction via spectral factorization. ArXiv E-Print 2103.13194. https://arxiv.org/abs/2103.13194
    31. 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
    32. 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 (Online First). https://doi.org/10.1177/1094342021990433
    33. 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
  2. 2020

    1. 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
    2. 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
    3. 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
    4. 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 International Symposium on Mixed and Augmented Reality (ISMAR). https://doi.org/10.1109/ISMAR50242.2020.00085
    5. 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
    6. Xiao, S., Oladyshkin, S., & Nowak, W. (2020). Forward-reverse switch between density-based and regional sensitivity analysis. Applied Mathematical Modelling, 84, 377--392.
    7. 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.
    8. 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
    9. 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
    10. 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
    11. 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
    12. 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 2020, 1--16. https://doi.org/10.1038/s41418-020-0512-5
    13. 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
    14. 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
    15. 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. https://doi.org/10.1038/s41418-020-0559-3
    16. 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.
    17. Stöhr, D., & Rehm, M. (2020). Linking hyperosmotic stress and apoptotic sensitivity. The FEBS Journal, febs.15520. https://doi.org/10.1111/febs.15520
    18. 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., & others. (2020). Stress-induced TRAILR2 expression overcomes TRAIL resistance in cancer cell spheroids. Cell Death & Differentiation, 1--16.
    19. 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.
    20. 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
    21. 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.
    22. 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. DaRUS. https://doi.org/10.18419/DARUS-680
    23. 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.
    24. 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
    25. Salm, M., Barzen, J., Leymann, F., & Weder, B. (2020). 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
    26. Rösinger, C. A., & Scherer, C. W. (2020). Lifting to Passivity for $H_2$-Gain-Scheduling Synthesis with Full Block Scalings. https://arxiv.org/abs/2001.05740
    27. 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
    28. Reutzsch, J., Kieffer-Roth, C., & Weigand, W. (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
    29. 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
    30. 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
    31. 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
    32. 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
    33. 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
    34. 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
    35. 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
    36. 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
    37. Martin, T., & Allgöwer, F. (2020). Data-driven surrogate models for LTI systems via saddle-point dynamics.
    38. 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
    39. Martin, T., & Allgöwer, F. (2020). Iterative data-driven inference of nonlinearity measures via successive graph approximation. 2020 59th IEEE Conference on Decision and Control (CDC), 4760–4765. https://doi.org/10.1109/CDC42340.2020.9304285
    40. 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.
    41. Kurz, M., & Beck, A. (2020). A machine learning framework for LES closure terms. https://doi.org/10.13140/RG.2.2.32569.19047
    42. 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
    43. 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
    44. 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
    45. 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
    46. 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
    47. 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
    48. 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. https://doi.org/10.1145/3399715.3399814
    49. 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
    50. 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
    51. 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.
    52. 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
    53. 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
    54. Gygli, G., & Pleiss, J. (2020). Simulation foundry: Automated and FAIR molecular modeling. Journal of Chemical Information and Modeling, 60(4), 1922--1927.
    55. Gygli, G., Xu, X., & Pleiss, J. (2020). Meta-analysis of viscosity of aqueous deep eutectic solvents and their components. Sci Rep, 10, 21395–21395.
    56. 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
    57. 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
    58. 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
    59. Fullstone, G., Bauer, T. L., Guttà, C., Salvucci, M., Prehn, J. H., & 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, 1--15.
    60. 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. https://doi.org/10.1038/s41418-020-0545-9
    61. 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.
    62. 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
    63. 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
    64. 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.
    65. 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
    66. Diestelkämper, R., & Herschel, M. (2020). Distributed Tree-Pattern Matching in Big Data Analytics Systems. Conference on Advances in Databases and Information Systems (ADBIS), 12245, 171--186. https://doi.org/10.1007/978-3-030-54832-2_14
    67. 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
    68. Diestelkämper, R., & Herschel, M. (2020). Tracing nested data with structural provenance for big data analytics. Proceedings of the 23nd International Conference on Extending Database Technology, EDBT 2020, Copenhagen, Denmark, March 30 - April 02, 2020, 253--264. https://doi.org/10.5441/002/edbt.2020.23
    69. Denzel, A., & Kästner, J. (2020). Hessian Matrix Update Scheme for Transition State Search Based on Gaussian Process Regression. Journal of Chemical Theory and Computation, 16(8), 5083--5089. https://doi.org/10.1021/acs.jctc.0c00348
    70. 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.
    71. 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
    72. 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
    73. 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
    74. Brencher, L., & Barth, A. (2020). Hyperbolic Conservation Laws with Stochastic Discontinuous Flux Functions. International Conference on Finite Volumes for Complex Applications, 265--273.
    75. 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
    76. 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), Article 7. https://doi.org/10.1029/2019wr026966
    77. Beck, A., & Kurz, M. (2020). A Perspective on Machine Learning Methods in Turbulence Modelling. https://doi.org/10.13140/RG.2.2.17469.69608
    78. Bauer, T., Buchholz, P., & Pleiss, J. (2020). The modular structure of α/β-hydrolases. FEBS J, 287, 1035–1053.
  3. 2019

    1. 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
    2. 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
    3. 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
    4. 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.
    5. 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
    6. 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
    7. 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.
    8. 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
    9. 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
    10. 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.
    11. 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.
    12. 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.
    13. 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
    14. 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.
    15. Oladyshkin, S., & Nowak, W. (2019). The Connection between Bayesian Inference and Information Theory for Model Selection, Information Gain and Experimental Design. Entropy, 21(11), 1081. https://doi.org/10.3390/e21111081
    16. 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
    17. 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
    18. Martin, T., & Allgöwer, F. (2019). Nonlinearity measures for data-driven system analysis and control. 2019 IEEE 58th Conference on Decision and Control (CDC), 3605–3610. https://doi.org/10.1109/CDC40024.2019.9029804
    19. 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
    20. 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
    21. 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
    22. Holicki, T., & Scherer, C. W. (2019). Output-Feedback Synthesis for a Class of Aperiodic Impulsive Systems. ArXiv:1910.03486 Math.OC. http://arxiv.org/abs/1910.03486
    23. 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
    24. 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
    25. 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
    26. 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
    27. 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.
    28. 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.
    29. 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
    30. Driess, D., Schmitt, S., & Toussaint, M. (2019). Active Inverse Model Learning with Error and Reachable Set Estimates. IROS, 1826--1833.
    31. Denzel, A., Haasdonk, B., & Kästner, J. (2019). Gaussian Process Regression for Minimum Energy Path Optimization and Transition State Search. The Journal of Physical Chemistry A, 123(44), 9600–9611. https://doi.org/10.1021/acs.jpca.9b08239
    32. 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
    33. 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.
    34. 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
  4. 2018

    1. Breitsprecher, K., Holm, C., & Kondrat, S. (2018). Charge Me Slowly, I Am in a Hurry: Optimizing Charge–Discharge Cycles in Nanoporous Supercapacitors. ACS Nano, 12(10), 9733--9741. https://doi.org/10.1021/acsnano.8b04785
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