Publications of PN 5

Publications PN 5

  1. 2024 (submitted)

    1. W. Nowak, T. Brünnette, M. Schalkers, and M. Möller, “Overdispersion in gate tomography: Experiments and continuous, two-scale random walk model on the Bloch sphere,” ACM Transactions on Quantum Computing.
    2. F. Ejaz, N. Wildt, T. Wöhling, and W. Nowak, “Estimating total groundwater storage and its associated uncertainty through spatiotemporal Kriging of groundwater-level data,” Journal of Hydrology.
  2. 2023

    1. D. Schneider, T. Schrader, and B. Uekermann, “Data-Parallel Radial-Basis Function Interpolation in preCICE,” in 10th edition of the International Conference on Computational Methods for Coupled Problems in Science and Engineering, M. Papadrakakis, S. B., and O. E., Eds., in 10th edition of the International Conference on Computational Methods for Coupled Problems in Science and Engineering. CIMNE, 2023. doi: 10.23967/c.coupled.2023.016.
    2. S. Schwindt et al., “Bayesian calibration points to misconceptions in three-dimensional hydrodynamic reservoir modelling,” Water Resources Research, vol. 59, p. e2022WR033660, 2023, doi: https://doi.org/10.1029/2022WR033660.
    3. 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, vol. 59, no. 7, Art. no. 7, Jul. 2023, doi: 10.1029/2022WR033711.
    4. G. Chourdakis, D. Schneider, and B. Uekermann, “OpenFOAM-preCICE: Coupling OpenFOAM with External Solvers for Multi-Physics Simulations,” OpenFOAM® Journal, vol. 3, pp. 1–25, Feb. 2023, doi: 10.51560/ofj.v3.88.
    5. L. Zhang, W. Nowak, S. Oladyshkin, Y. Wang, and J. Cai, “Opportunities and challenges in $CO_2$ geologic utilization and storage,” Advances in Geo-Energy Research, vol. 8, no. 3, Art. no. 3, Jul. 2023, [Online]. Available: https://doi.org/10.46690/ager.2023.06.01
    6. P. Buchfink, S. Glas, and B. Haasdonk, “Symplectic Model Reduction of Hamiltonian Systems on Nonlinear Manifolds and Approximation with Weakly Symplectic Autoencoder,” SIAM Journal on Scientific Computing, vol. 45, no. 2, Art. no. 2, Mar. 2023, doi: 10.1137/21m1466657.
    7. M. Oesting, A. Rapp, and E. Spodarev, “Detection of long range dependence in the time domain for (in)finite-variance time series,” Statistics, pp. 1–28, Dec. 2023, doi: 10.1080/02331888.2023.2287749.
    8. I. Kröker, S. Oladyshkin, and I. Rybak, “Global sensitivity analysis using multi-resolution polynomial chaos expansion for coupled Stokes-Darcy flow problems,” Computational Geosciences, 2023, doi: 10.1007/s10596-023-10236-z.
    9. R. Merkle and A. Barth, “On Properties and Applications of Gaussian Subordinated Lévy Fields,” Methodology and Computing in Applied Probability, vol. 25, p. 62, 2023, doi: 10.1007/s11009-023-10033-2.
    10. R. Kohlhaas, I. Kröker, S. Oladyshkin, and W. Nowak, “Gaussian active learning on multi-resolution arbitrary polynomial chaos emulator: concept for bias correction, assessment of surrogate reliability and its application to the carbon dioxide benchmark,” Computational Geosciences, vol. 27, no. 3, Art. no. 3, 2023, doi: doi:10.1007/s10596-023-10199-1.
    11. H. Sharma, H. Mu, P. Buchfink, R. Geelen, S. Glas, and B. Kramer, “Symplectic model reduction of Hamiltonian systems using data-driven quadratic manifolds,” Computer Methods in Applied Mechanics and Engineering, vol. 417, p. 116402, Dec. 2023, doi: 10.1016/j.cma.2023.116402.
    12. 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,” Journal of Computational Physics, p. 112210, 2023, doi: https://doi.org/10.1016/j.jcp.2023.112210.
    13. 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, vol. 166, pp. 85–104, Sep. 2023, doi: 10.1016/j.neunet.2023.06.036.
    14. 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.
    15. K. Mouris et al., “Stability criteria for Bayesian calibration of reservoir sedimentation models,” Modeling Earth Systems and Environment, 2023, doi: 10.1007/s40808-023-01712-7.
    16. J. Rettberg et al., “Port-Hamiltonian fluid–structure interaction modelling and structure-preserving model order reduction of a classical guitar,” Mathematical and Computer Modelling of Dynamical Systems, vol. 29, no. 1, Art. no. 1, 2023, doi: 10.1080/13873954.2023.2173238.
  3. 2022

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    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. R. Merkle and A. Barth, “Subordinated Gaussian random fields in elliptic partial differential equations,” Stochastics and Partial Differential Equations: Analysis and Computations, vol. 11, pp. 819–867, 2022, doi: 10.1007/s40072-022-00246-w.
    7. A. Barth and A. Stein, “Numerical analysis for time-dependent advection-diffusion problems with random discontinuous coefficients,” ESAIM: M2AN, vol. 56, no. 5, Art. no. 5, 2022, doi: 10.1051/m2an/2022054.
    8. 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.
    9. 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.
    10. R. Merkle and A. Barth, “On Some Distributional Properties of Subordinated Gaussian Random Fields,” Methodology and Computing in Applied Probability, vol. 24, pp. 2661–2688, 2022, doi: 10.1007/s11009-022-09958-x.
    11. L. Chavez Rodriguez, A. González-Nicolás, B. Ingalls, W. Nowak, S. Xiao, and H. Pagel, “Optimal design of experiments to improve the characterization of atrazine degradation pathways in soil,” European Journal of Soil Science, vol. 73, no. 1, Art. no. 1, 2022, doi: 10.1111/ejss.13211.
    12. 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.
    13. 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.
    14. J. Eller, T. Sauerborn, B. Becker, I. Buntic, J. Gross, and R. Helmig, “Modeling Subsurface Hydrogen Storage With Transport Properties From Entropy Scaling Using the PC‐SAFT Equation of State,” Water Resources Research, vol. 58, no. 4, Art. no. 4, 2022, doi: 10.1029/2021wr030885.
    15. 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.
    16. 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.
    17. 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.
    18. R. Merkle and A. Barth, “Multilevel Monte Carlo estimators for elliptic PDEs with Lévy-type diffusion coefficient,” BIT Numerical Mathematics, vol. 62, pp. 1279–1317, 2022, doi: 10.1007/s10543-022-00912-4.
    19. A. Gonzalez-Nicolas et al., “Optimal Exposure Time in Gamma-Ray Attenuation Experiments for Monitoring Time-Dependent Densities,” Transport in Porous Media, vol. 143, no. 2, Art. no. 2, 2022, doi: 10.1007/s11242-022-01777-5.
  4. 2021

    1. T. Praditia, M. Karlbauer, S. Otte, S. Oladyshkin, M. Butz, and W. Nowak, “Finite Volume Neural Network: Modeling Subsurface Contaminant Transport,” in Deep Learning for Simulation ICLR Workshop 2021, in Deep Learning for Simulation ICLR Workshop 2021. 2021. [Online]. Available: https://simdl.github.io/files/33.pdf
    2. 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.
    3. 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.
    4. S. Reuschen, A. Guthke, and W. Nowak, “The Four Ways to Consider Measurement Noise in Bayesian Model Selection - And Which One to Choose,” Water Resources Research, vol. 57, no. 11, Art. no. 11, 2021.
    5. I. Banerjee, A. Guthke, C. J. C. Van de Ven, K. G. Mumford, and W. Nowak, “Overcoming the model-data-fit problem in porous media: A quantitative method to compare invasion-percolation models to high-resolution data,” Water Resources Research, vol. 57, no. 7, Art. no. 7, 2021, doi: 10.1029/2021WR029986.
    6. S. Scheurer et al., “Surrogate-based Bayesian Comparison of Computationally Expensive Models: Application to Microbially Induced Calcite Precipitation,” Computational Geosciences, vol. 25, pp. 1899–1917, 2021.
    7. S. Xiao, T. Xu, S. Reuschen, W. Nowak, and H.-J. H. Franssen, “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, vol. 57, p. e2021WR030313, 2021, doi: 10.1029/2021WR030313.
    8. K. Cheng, S. Xiao, X. Zhang, S. Oladyshkin, and W. Nowak, “Resampling method for reliability-based design optimization based on thermodynamic integration and parallel tempering,” Mechanical Systems and Signal Processing, vol. 156, p. 107630, 2021, doi: 10.1016/j.ymssp.2021.107630.
  5. 2020

    1. 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
    2. S. Oladyshkin et al., “Uncertainty quantification using Bayesian arbitrary polynomial chaos for computationally demanding environmental modelling: conventional, sparse and adaptive strategy,” in Computational Methods in Water Resources (CMWR), in Computational Methods in Water Resources (CMWR). 2020.
    3. L. Brencher and A. Barth, “Hyperbolic Conservation Laws with Stochastic Discontinuous Flux Functions,” Finite Volumes for Complex Applications IX : Methods, Theoretical Aspects, Examples, no. 323. in Finite Volumes for Complex Applications IX : Methods, Theoretical Aspects, Examples. Springer, pp. 265–273, 2020. doi: 10.1007/978-3-030-43651-3_23.
    4. I. Guisandez, J. I. Perez-Diaz, W. Nowak, and J. Haas, “Should environmental constraints be considered in linear programming based water value calculators?,” International Journal of Electrical Power & Energy Systems, vol. 117, no. 105662, Art. no. 105662, May 2020, doi: 10.1016/j.ijepes.2019.105662.
    5. D. Erdal, S. Xiao, W. Nowak, and O. Cirpka, “Sampling Behavioral Model Parameters for Ensemble-based Sensitivity Analysis using Gaussian Process Emulation and Active Subspaces,” Stochastic Environmental Research and Risk Assessment, vol. 34, pp. 1813–1830, 2020, doi: 10.1007/s00477-020-01867-0.
    6. M. Sinsbeck, M. Höge, and W. Nowak, “Exploratory-phase-free estimation of GP hyperparameters in sequential design methods - at the example of Bayesian inverse problems,” Frontiers in Artificial Intelligence, section AI in Food, Agriculture and Water, vol. 3, no. 52, Art. no. 52, 2020, doi: 10.3389/frai.2020.00052.
    7. T. Xu, S. Reuschen, W. Nowak, and H.-J. H. Franssen, “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, vol. 56, no. 8, Art. no. 8, 2020, doi: 10.1029/2020WR027110.
    8. 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, 2020, doi: 10.1029/2019WR026966.
    9. A. Schäfer Rodrigues Silva, A. Guthke, M. Höge, O. A. Cirpka, and W. Nowak, “Strategies for simplifying reactive transport models - a Bayesian model comparison,” Water Resources Research, vol. 56, p. e2020WR028100, 2020, doi: 10.1029/2020WR028100.
    10. 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.
  6. 2019

    1. 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.
    2. 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.

Software PN 5

    Data PN 5

    1. 2023

      1. J. Rettberg et al., “Replication Data for: Port-Hamiltonian Fluid-Structure Interaction Modeling and Structure-Preserving Model Order Reduction of a Classical Guitar.” 2023. doi: 10.18419/darus-3248.

    Project Network Coordinators

    This image shows Andrea Barth

    Andrea Barth

    Prof. Dr.

    Computational Methods for Uncertainty Quantification

    [Photo: SimTech/Max Kovalenko]

    This image shows Dominik Göddeke

    Dominik Göddeke

    Prof. Dr. rer. nat.

    Computational Mathematics for Complex Simulations in Science and Engineering

    [Photo: SimTech/Max Kovalenko]

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