Adaptive surrogate models in heterogeneous computer environments

PN 7-6 (II)

Project description

High-fidelity simulation models provide insights into complex systems involving high computational effort. Intelligent surrogate models can reuse knowledge gained from former simulation results. Hence, cheap yet accurate surrogates enable accessible model insights anywhere by approximating and accelerating the expensive models.

A main goal of this project is to implement holistic surrogate models of dynamic bio- and structural dynamical systems on weak hardware by significantly reducing computational costs. For this purpose, we develop novel and adaptive surrogate modeling techniques that combine nonlinear dimensionality reduction and coordinate identification with approximations of latent dynamics to save resources. Clustering strategies and error estimators can be used to identify time and space domains of interest; based on good provenance data, we select optimal surrogate models based on the current demand and domains. In addition, those surrogate models can incorporate sensor measurement feedback to synchronize the virtual models with the real world and improve their accuracy and robustness.

Project information

Project title Adaptive surrogate models in heterogeneous computer environments
Project leaders Jörg Fehr (Manfred Bischoff, Tanja Blascheck)
Project staff Jonas Kneifl, doctoral researcher
Project duration May 2023 - December 2025
Project number PN 7-6 (II)

Publications PN 7-6 and PN 7-6 (II)

  1. 2023

    1. J. Hay et al., “Application of data-driven surrogate models for active human model response prediction and restraint system optimization,” Frontiers in applied mathematics and statistics, vol. 9, pp. 1–16, 2023, doi: 10.3389/fams.2023.1156785.
    2. J. Kneifl, D. Rosin, O. Avci, O. Röhrle, and J. Fehr, “Low-dimensional data-based surrogate model of a continuum-mechanical musculoskeletal system based on non-intrusive model order reduction,” Archive of Applied Mechanics, vol. 93, pp. 3637–3663, 2023, doi: 10.1007/s00419-023-02458-5.
  2. 2022

    1. J. Nicodemus, J. Kneifl, J. Fehr, and B. Unger, “Physics-informed Neural Networks-based Model Predictive Control for Multi-link Manipulators,” IFAC-PapersOnLine, vol. 55, no. 20, Art. no. 20, 2022, doi: 10.1016/j.ifacol.2022.09.117.
  3. 2021

    1. J. Kneifl, D. Grunert, and J. Fehr, “A non-intrusive nonlinear model reduction method for structural dynamical problems based on machine learning,” International Journal for Numerical Methods in Engineering, Apr. 2021, doi: 10.1002/nme.6712.
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