Publications of PN 5

  1. 2022 (submitted)

    1. C. C. Horuz et al., “Inferring Boundary Conditions in Finite Volume Neural Networks.”
  2. 2022

    1. M. Takamoto et al., “PDEBench: An Extensive Benchmark for Scientific Machine Learning,” 2022.
    2. 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.
    3. J. Rettberg et al., “Port-Hamiltonian Fluid-Structure Interaction Modeling and Structure-Preserving Model Order Reduction of a Classical Guitar.” 2022. doi:
    4. 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:
  3. 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. 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.
    3. R. Leiteritz, P. Buchfink, B. Haasdonk, and D. Pflüger, “Surrogate-data-enriched Physics-Aware Neural Networks.” 2021.
    4. 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.
    5. B. Hilder, “Modulating traveling fronts in a dispersive Swift-Hohenberg equation coupled to an additional conservation law.” 2021.
    6. R. Herkert, “Model Order Reduction and Geometry Parametrization for Linear Elasticity.” 2021.
    7. B. Haasdonk, M. Ohlberger, and F. Schindler, “An adaptive model hierarchy for data-augmented training of kernel models for reactive flow.” 2021.
    8. M. Göhring, “Model Order Reduction with Kernel Autoencoders.” 2021.
    9. T. Ehring, “Feedback control for dynamic soft tissue systems by a surrogate of the value function.” 2021.
    10. P. Buchfink, S. Glas, and B. Haasdonk, “Symplectic Model Reduction of Hamiltonian Systems on Nonlinear Manifolds.” 2021. doi:
    11. 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.
    12. 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.
  4. 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.
  5. 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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