Publications of PN 5

S. Oladyshkin, T. Praditia, I. Kroeker, F. Mohammadi, W. Nowak, and S. Otte, “The Deep Arbitrary Polynomial Chaos Neural Network or how Deep Artificial Neural Networks could benefit from Data-Driven Homogeneous Chaos Theory,” Neural Networks, 2023, [Online]. Available: https://arxiv.org/abs/2306.14753

C. C. Horuz et al., “Physical Domain Reconstruction with Finite Volume Neural Networks,” Applied Artificial Intelligence, vol. 37, no. 1, Art. no. 1, 2023, doi: https://doi.org/10.1080/08839514.2023.2204261.

S. Xiao and W. Nowak, “Reliability sensitivity analysis based on a two-stage Markov chain Monte Carlo simulation,” Aerospace Science and Technology, vol. 130, p. 107938, 2022, doi: 10.1016/j.ast.2022.107938.

V. Wagner, B. Castellaz, M. Oesting, and N. Radde, “Quasi-Entropy Closure: A Fast and Reliable Approach to Close the Moment Equations of the Chemical Master Equation,” Bioinformatics, vol. 38, no. 18, Art. no. 18, 2022, doi: 10.1093/bioinformatics/btac501.

M. Takamoto et al., “PDEBench: An Extensive Benchmark for Scientific Machine Learning,” in 36th Conference on Neural Information Processing Systems (NeurIPS 2022) Track on Datasets and Benchmarks, in 36th Conference on Neural Information Processing Systems (NeurIPS 2022) Track on Datasets and Benchmarks. 2022.

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

A. Schäfer Rodrigues Silva et al., “Diagnosing Similarities in Probabilistic Multi-Model Ensembles - an Application to Soil-Plant-Growth-Modeling,” Modeling Earth Systems and Environment, vol. 8, pp. 5143–5175, 2022, doi: 10.1007/s40808-022-01427-1.

A. Rodriguez-Pretelin, E. Morales-Casique, and W. Nowak, “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, vol. 162, p. 104146, 2022, doi: 10.1016/j.advwatres.2022.104146.

J. Rettberg et al., “Port-Hamiltonian Fluid-Structure Interaction Modeling and Structure-Preserving Model Order Reduction of a Classical Guitar.” 2022. doi: https://doi.org/10.48550/arXiv.2203.10061.

T. Praditia, M. Karlbauer, S. Otte, S. Oladyshkin, M. V. Butz, and W. Nowak, “Learning Groundwater Contaminant Diffusion-Sorption Processes with a Finite Volume Neural Network,” Water Resources Research, vol. 58, no. 12, Art. no. 12, 2022, doi: 10.1029/2022WR033149.

M. Oesting and K. Strokorb, “A Comparative Tour through the Simulation Algorithms for Max-Stable Processes,” Statistical Science, vol. 37, no. 1, Art. no. 1, 2022, doi: 10.1214/20-STS820.

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

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

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

R. Leiteritz, P. Buchfink, B. Haasdonk, and D. Pflüger, “Surrogate-data-enriched Physics-Aware Neural Networks,” in Proceedings of the Northern Lights Deep Learning Workshop 2022, in Proceedings of the Northern Lights Deep Learning Workshop 2022, vol. 3. Mar. 2022. doi: 10.7557/18.6268.

J. Legrand, P. Naveau, and M. Oesting, “Evaluation of binary classifiers for asymptotically dependent and independent extremes,” 2022.

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

I. Kröker and S. Oladyshkin, “Arbitrary Multi-Resolution Multi-Wavelet-based Polynomial Chaos Expansion for Data-Driven Uncertainty Quantification,” Reliability Engineering & System Safety, vol. 222, p. 108376, 2022, doi: 10.1016/j.ress.2022.108376.

M. Klink, “Time Error Estimators and Adaptive Time-stepping Schemes,” bathesis, 2022.

M. Karlbauer, T. Praditia, S. Otte, S. Oladyshkin, W. Nowak, and M. V. Butz, “Composing Partial Differential Equations with Physics-Aware Neural Networks,” in Proceedings of the 39th International Conference on Machine Learning, in Proceedings of the 39th International Conference on Machine Learning. Baltimore, USA, 2022.

H. Hsueh, A. Guthke, T. Wöhling, and W. Nowak, “Diagnosis of model-structural errors with a sliding time-window Bayesian analysis,” Water Resources Research, vol. 58, p. e2021WR030590, 2022, doi: doi:10.1029/2021WR030590.

C. C. Horuz et al., “Inferring Boundary Conditions in Finite Volume Neural Networks,” in International Conference on Artificial Neural Networks 2022, in International Conference on Artificial Neural Networks 2022. 2022.

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

P. Buchfink, S. Glas, and B. Haasdonk, “Optimal Bases for Symplectic Model Order Reduction of Canonizable Linear Hamiltonian Systems,” in IFAC-PapersOnLine, in IFAC-PapersOnLine, vol. 55. 2022, pp. 463--468. doi: 10.1016/j.ifacol.2022.09.138.

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

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

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

A. Rörich, T. A. Werthmann, D. Göddeke, and L. Grasedyck, “Bayesian inversion for electromyography using low-rank tensor formats,” Inverse Problems, vol. 37, no. 5, Art. no. 5, Mar. 2021, doi: 10.1088/1361-6420/abd85a.

M. Osorno, M. Schirwon, N. Kijanski, R. Sivanesapillai, H. Steeb, and D. Göddeke, “A cross-platform, high-performance SPH toolkit for image-based flow simulations on the pore scale of porous media,” Computer Physics Communications, vol. 267, no. 108059, Art. no. 108059, Oct. 2021, doi: 10.1016/j.cpc.2021.108059.

R. Herkert, “Model Order Reduction and Geometry Parametrization for Linear Elasticity.” 2021.

B. Haasdonk, M. Ohlberger, and F. Schindler, “An adaptive model hierarchy for data-augmented training of kernel models for reactive flow.” 2021.

M. Göhring, “Model Order Reduction with Kernel Autoencoders.” 2021.

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

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

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

T. Benacchio et al., “Resilience and fault tolerance in high-performance computing for numerical weather and climate prediction,” The International Journal of High Performance Computing Applications, vol. 35, no. 4, Art. no. 4, Feb. 2021, doi: 10.1177/1094342021990433.

S. Xiao, S. Oladyshkin, and W. Nowak, “Forward-reverse switch between density-based and regional sensitivity analysis,” Applied Mathematical Modelling, vol. 84, pp. 377--392, 2020.

S. Xiao, S. Oladyshkin, and W. Nowak, “Reliability analysis with stratified importance sampling based on adaptive Kriging,” Reliability Engineering & System Safety, vol. 197, p. 106852, May 2020, doi: 10.1016/j.ress.2020.106852.

T. Praditia, T. Walser, S. Oladyshkin, and W. Nowak, “Improving Thermochemical Energy Storage Dynamics Forecast with Physics-Inspired Neural Network Architecture,” Energies, vol. 13, no. 15, Art. no. 15, Jul. 2020, doi: 10.3390/en13153873.

S. Oladyshkin, F. Mohammadi, I. Kroeker, and W. Nowak, “Bayesian3 Active Learning for the Gaussian Process Emulator Using Information Theory,” Entropy, vol. 22, no. 8, Art. no. 8, Aug. 2020, doi: 10.3390/e22080890.

S. Müller, “Symplectic Neural Networks.” 2020.

B. Hilder, “Modulating traveling fronts for the Swift-Hohenberg equation in the case of an additional conservation law,” Journal of Differential Equations, vol. 269, no. 5, Art. no. 5, Aug. 2020, doi: 10.1016/j.jde.2020.03.033.

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

L. Brencher and A. Barth, “Hyperbolic Conservation Laws with Stochastic Discontinuous Flux Functions,” in International Conference on Finite Volumes for Complex Applications, in International Conference on Finite Volumes for Complex Applications. Springer, 2020, pp. 265--273.

F. Beckers, A. Heredia, M. Noack, W. Nowak, S. Wieprecht, and S. Oladyshkin, “Bayesian Calibration and Validation of a Large-Scale and Time-Demanding Sediment Transport Model,” Water Resources Research, vol. 56, no. 7, Art. no. 7, Jul. 2020, doi: 10.1029/2019wr026966.

S. Xiao, S. Reuschen, G. Köse, S. Oladyshkin, and W. Nowak, “Estimation of small failure probabilities based on thermodynamic integration and parallel tempering,” Mechanical Systems and Signal Processing, vol. 133, p. 106248, Nov. 2019, doi: 10.1016/j.ymssp.2019.106248.

S. Oladyshkin and W. Nowak, “The Connection between Bayesian Inference and Information Theory for Model Selection, Information Gain and Experimental Design,” Entropy, vol. 21, no. 11, Art. no. 11, Nov. 2019, doi: 10.3390/e21111081.