Publications of 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. 2024

    1. S. Xiao and W. Nowak, “Failure probability estimation with failure samples: An extension of the two-stage Markov chain Monte Carlo simulation,” Mechanical Systems and Signal Processing, vol. 212, p. 111300, Feb. 2024, doi: https://doi.org/10.1016/j.ymssp.2024.111300.
  3. 2023 (submitted)

    1. F. Mohammadi et al., “Uncertainty-aware Validation Benchmarks for Coupling Free Flow and Porous-Medium Flow,” Water Resources Research.
  4. 2023

    1. 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.
    2. N. Wildt, S. Scheurer, W. Nowak, and C. Haslauer, “Learning PFAS mechanisms with a FInite Volume Neural Network (FINN),” in Fall Meeting 2023, in Fall Meeting 2023. San Francisco, CA, USA: American Geophysical Union (AGU), Dec. 2023.
    3. A. Wagner, A. Sonntag, S. Reuschen, W. Nowak, and W. Ehlers, “Hydraulically induced fracturing in heterogeneous porous media using a TPM-phase-field model and geostatistics,” Proceedings in Applied Mathematics and Mechanics, vol. 23, p. e202200118, 2023, doi: https://doi.org/10.1002/pamm.202200118.
    4. 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.
    5. 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.
    6. 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.
    7. J. Rettberg, D. Wittwar, P. Buchfink, R. Herkert, J. Fehr, and B. Haasdonk, “Improved a posteriori Error Bounds for Reduced port-Hamiltonian Systems.” 2023. doi: https://doi.org/10.48550/arXiv.2303.17329.
    8. 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.
    9. 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.
    10. 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
    11. M. Oesting and O. Wintenberger, “https://cnrs.hal.science/hal-02958799v2,” 2023.
    12. M. Oesting and O. Wintenberger, “Estimation of the Spectral Measure from Convex Combinations of Regularly Varying Random Vectors.” 2023. [Online]. Available: https://cnrs.hal.science/hal-02958799v2
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. J. Lederer and M. Oesting, “Extremes in High Dimensions: Methods and Scalable Algorithms.” 2023.
    18. 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.
    19. 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.
    20. 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.
    21. R. R. Herkert, P. Buchfink, B. Haasdonk, J. Rettberg, and J. C. Fehr, “Randomized Symplectic Model Order Reduction for Hamiltonian Systems,” pp. 1–8, 2023, doi: 10.48550/arXiv.2303.04036.
    22. F. Ejaz, A. Guthke, T. Wöhling, and W. Nowak, “Comprehensive uncertainty analysis for surface water and groundwater projections under climate change based on a lumped geo-hydrological model,” Journal of Hydrology, vol. 626, 2023, doi: https://doi.org/10.1016/j.jhydrol.2023.130323.
    23. C. Dibak, W. Nowak, F. Dürr, and K. Rothermel, “Using Surrogate Models and Data Assimilation for Efficient Mobile Simulations,” IEEE Transactions on Mobile Computing, vol. 22, no. 3, Art. no. 3, 2023, doi: 10.1109/TMC.2021.3108750.
    24. 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.
    25. 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.
    26. P. Buchfink, S. Glas, B. Haasdonk, and B. Unger, “Model reduction on manifolds: A differential geometric framework,” arXiv e-prints, 2023. [Online]. Available: https://arxiv.org/abs/2312.01963
    27. 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.
    28. P. Buchfink, S. Glas, and B. Haasdonk, “Approximation Bounds for Model Reduction on Polynomially Mapped Manifolds,” arXiv e-prints, 2023. [Online]. Available: https://arxiv.org/abs/2312.00724
    29. C. A. Beschle and A. Barth, “Quasi continuous level Monte Carlo for random elliptic PDEs,” 2023. [Online]. Available: https://arxiv.org/abs/2303.08694
    30. I. Banerjee, A. Guthke, C. J. Van De Ven, K. G. Mumford, and W. Nowak, “Comparison of Four Competing Invasion Percolation Models for Gas Flow in Porous Media,” Authorea Preprints, 2023.
  5. 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,” 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.
    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, I. Lirkov and S. Margenov, Eds., in Large-Scale Scientific Computing. Springer International Publishing, 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: https://doi.org/10.48550/arXiv.2203.10061.
    8. J. Rettberg et al., “Port-Hamiltonian Fluid-Structure Interaction Modeling and Structure-Preserving Model Order Reduction of a Classical Guitar,” pp. 1–27, 2022, doi: 10.48550/arXiv.2203.10061.
    9. 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.
    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. R. Merkle and A. Barth, “Subordinated Gaussian random fields in elliptic partial differential equations,” Stochastics and partial differential equations, 2022, doi: 10.1007/s40072-022-00246-w.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. J. Legrand, P. Naveau, and M. Oesting, “Evaluation of binary classifiers for asymptotically dependent and independent extremes,” 2022.
    19. 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.
    20. 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.
    21. M. Klink, “Time Error Estimators and Adaptive Time-stepping Schemes,” bathesis, 2022.
    22. 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.
    23. 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.
    24. 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.
    25. 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.
    26. 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.
    27. 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.
    28. 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.
    29. 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.
    30. B. Becker, B. Guo, I. Buntic, B. Flemisch, and R. Helmig, “An adaptive hybrid vertical equilibrium/full dimensional model for compositional multiphase flow,” Water Resources Research, p. e2021WR030990, 2022, doi: 10.1029/2021WR030990.
    31. 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.
    32. 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.
  6. 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,” 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. Scheurer et al., “Surrogate-based Bayesian comparison of computationally expensive models: application to microbially induced calcite precipitation,” Computational Geosciences, vol. 25, no. 6, Art. no. 6, 2021, doi: 10.1007/s10596-021-10076-9.
    4. 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.
    5. T. Praditia, S. Oladyshkin, and W. Nowak, “Physics Informed Neural Network for porous media modelling,” Stuttgart, Germany: InterPore German Chapter Meeting 2021, Feb. 2021.
    6. T. Praditia, S. Oladyshkin, and W. Nowak, “Universal Differential Equation for Diffusion-Sorption Problem in Porous Media Flow,” online: EGU General Assembly 2021, Apr. 2021.
    7. 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.
    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 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.
    13. 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.
    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.
  7. 2020

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. S. Müller, “Symplectic Neural Networks.” 2020.
    8. M. Höge, A. Guthke, and W. Nowak, “Bayesian Model Weighting: The Many Faces of Model Averaging,” Water, vol. 12, no. 2, Art. no. 2, 2020, doi: 10.3390/w12020309.
    9. 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.
    10. 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.
    11. 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.
    12. 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
    13. L. Brencher and A. Barth, “Hyperbolic Conservation Laws with Stochastic Discontinuous Flux Functions,” in Finite Volumes for Complex Applications IX : Methods, Theoretical Aspects, Examples, R. Klöfkorn, E. Keilegavlen, F. A. Radu, and J. Fuhrmann, Eds., in Finite Volumes for Complex Applications IX : Methods, Theoretical Aspects, Examples. Springer, 2020, pp. 265–273. doi: 10.1007/978-3-030-43651-3_23.
    14. 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.
  8. 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.

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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