Uncertainty quantification by physics- and data-based models for mechanical systems

PN 5-7

Project description

Due to resource-limitations in the context of uncertainty quantification (UQ), full simulation models are not applicable. This is particularly true in the target application context of multi-phase flow in porous media or multiphysics muscle-modelling. Hence sophisticated surrogate modelling strategies are indispensable. This project has an accompanying project PN 6-2, which is requested in PN 6 and is aiming at machine learning based surrogates. In contrast, the PN 5 project will be aiming at adaptive reduction techniques that enable automatic adaption to external resource limitations (runtime, accuracy) and respect physical constraints. We will focus on projection-based model order reduction (MOR) and sparse grid (SG) approximation. We will consider parametric models that can both be treated in a deterministic as well as a stochastic context, and hence enable uncertainty quantification (UQ). In addition to parametric uncertainty, also the surrogate models themselves have inherent inaccuracy, and we will aim at controlling those errors by deterministic error bounds. In order to improve over UQ predictions by the reduced models, the learning results from the PN6 partner project will enable multifidelity strategies.

Project information

Project title Uncertainty Quantification by Physics- and Data-Based Models for Mechanical Systems
Project leaders Bernard Haasdonk (Dirk Pflüger)
Project duration December 2019 - May 2023
Project number PN 5-7

Publications PN 5-7

  1. 2023

    1. 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.
    2. 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.
    3. 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.
  2. 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. 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.
    3. 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.
  3. 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
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