Publications of PN 5

  1. 2023

    1. N. Hornischer, “Model Order Reduction with Dynamically Transformed Modes for Electrophysiological Simulations,” GAMM Archive for Students, 2023.
  2. 2022 (submitted)

    1. C. C. Horuz et al., “Inferring Boundary Conditions in Finite Volume Neural Networks.”
  3. 2022

    1. 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.
    2. 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.
    3. M. Takamoto et al., “PDEBench: An Extensive Benchmark for Scientific Machine Learning,” 2022.
    4. 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, 2022, pp. 402--409.
    5. 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.
    6. 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.
    7. J. Rettberg et al., “Port-Hamiltonian Fluid-Structure Interaction Modeling and Structure-Preserving Model Order Reduction of a Classical Guitar.” 2022. doi:
    8. 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.
    9. S. Oladyshkin, T. Praditia, I. Kröker, 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, 2022.
    10. 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.
    11. 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.
    12. 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:
    13. 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.
    14. 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, Mar. 2022, vol. 3. doi: 10.7557/18.6268.
    15. J. Legrand, P. Naveau, and M. Oesting, “Evaluation of binary classifiers for asymptotically dependent and independent extremes,” 2022.
    16. 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.
    17. 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/
    18. M. Klink, “Time Error Estimators and Adaptive Time-stepping Schemes,” bathesis, 2022.
    19. M. Karlbauer, T. Praditia, S. Otte, S. Oladyshkin, W. Nowak, and M. V. Butz, “Composing Partial Differential Equations with Physics-Aware Neural Networks,” Baltimore, USA, 2022.
    20. 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.
    21. N. Hornischer, “Model Order Reduction with Transformed Modes for Electrophysiological Simulations,” bathesis, 2022.
    22. 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.
    23. P. Buchfink, S. Glas, and B. Haasdonk, “Optimal Bases for Symplectic Model Order Reduction of Canonizable Linear Hamiltonian Systems,” in IFAC-PapersOnLine, 2022, vol. 55, no. 20, pp. 463--468. doi: 10.1016/j.ifacol.2022.09.138.
    24. 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.
  4. 2021

    1. 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.
    2. J. Schmalfuss, C. Riethmüller, M. Altenbernd, K. Weishaupt, and D. Göddeke, “Partitioned coupling vs. monolithic block-preconditioning approaches for solving Stokes-Darcy systems,” 2021. doi: 10.23967/coupled.2021.043.
    3. 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.
    4. 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.
    5. R. Leiteritz, P. Buchfink, B. Haasdonk, and D. Pflüger, “Surrogate-data-enriched Physics-Aware Neural Networks.” 2021.
    6. M. Karlbauer, T. Praditia, S. Otte, S. Oladyshkin, W. Nowak, and M. V. Butz, “Composing Partial Differential Equations with Physics-Aware Neural Networks,” submitted to International Conference on Learning Representations, 2021.
    7. B. Hilder, “Modulating traveling fronts in a dispersive Swift-Hohenberg equation coupled to an additional conservation law.” 2021.
    8. R. Herkert, “Model Order Reduction and Geometry Parametrization for Linear Elasticity.” 2021.
    9. B. Haasdonk, M. Ohlberger, and F. Schindler, “An adaptive model hierarchy for data-augmented training of kernel models for reactive flow.” 2021.
    10. M. Göhring, “Model Order Reduction with Kernel Autoencoders.” 2021.
    11. T. Ehring, “Feedback control for dynamic soft tissue systems by a surrogate of the value function.” 2021.
    12. P. Buchfink, S. Glas, and B. Haasdonk, “Symplectic Model Reduction of Hamiltonian Systems on Nonlinear Manifolds.” 2021. doi:
    13. 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, 2021, vol. 139. doi: 10.1007/978-3-030-55874-1.
    14. 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.
  5. 2020

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. S. Müller, “Symplectic Neural Networks.” 2020.
    6. 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.
    7. 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, Aug. 2020, pp. 151--160. [Online]. Available:
    8. L. Brencher and A. Barth, “Hyperbolic Conservation Laws with Stochastic Discontinuous Flux Functions,” in International Conference on Finite Volumes for Complex Applications, 2020, pp. 265--273.
    9. 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.
  6. 2019

    1. 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.
    2. 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.

Project Network Coordinators

This image shows Andrea Barth

Andrea Barth

Prof. Dr.

[Photo: SimTech/Max Kovalenko]

This image shows Dominik Göddeke

Dominik Göddeke

Prof. Dr. rer. nat.

[Photo: SimTech/Max Kovalenko]

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