Reusage and reanalysis of simulation data in structural dynamics

PN 7-6

Project description

The aim of this project is the reusage of previous simulation data to approximate, accelerate and improve future simulations. To this end, the corresponding dynamic bio and structural mechanics problems are analysed under the aspect of data-based approximations. For each of the resulting problem classes the success of such an approach of data reuse is investigated.

Project information

Project title Reusage and reanalysis of simulation data in structural dynamics
Project leaders Jörg Fehr (Manfred Bischoff)
Project duration January 2020 - June 2023
Project number PN 7-6

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

  1. 2024

    1. J. Kneifl, J. Fehr, S. L. Brunton, and J. N. Kutz, “Multi-Hierarchical Surrogate Learning for Structural Dynamical Crash Simulations Using Graph Convolutional Neural Networks.” 2024.
    2. P. Rodegast, S. Maier, J. Kneifl, and J. Fehr, “On using machine learning algorithms for motorcycle collision detection,” Discover Applied Sciences, vol. 6, Art. no. 6, Jun. 2024, doi: 10.1007/s42452-024-06014-w.
    3. M. Millard, N. Stutzig, J. Fehr, and T. Siebert, “A benchmark of muscle models to length changes great and small,” Journal of the Mechanical Behavior of Biomedical Materials, vol. 160, p. 106740, 2024, doi: 10.1016/j.jmbbm.2024.106740.
    4. J. Kneifl, J. Fehr, S. L. Brunton, and J. N. Kutz, “Multi-hierarchical surrogate learning for explicit structural dynamical systems using graph convolutional neural networks,” Computational Mechanics, Oct. 2024, doi: 10.1007/s00466-024-02553-6.
    5. A. Strauß, J. Kneifl, A. Tkachuk, J. Fehr, and M. Bischoff, “Accelerated Non‐linear Stability Analysis Based on Predictions From Data‐Based Surrogate Models,” International Journal for Numerical Methods in Engineering, vol. 126, Art. no. 1, Dec. 2024, doi: 10.1002/nme.7649.

  2. 2023

    1. P. Rodegast, S. Maier, J. Kneifl, and J. Fehr, “Simulation Data from Motorcycle Sensors in Operational and Crash Scenarios,” 2023, doi: 10.18419/DARUS-3301.
    2. J. Kneifl and J. Fehr, “Crash Simulations of a Racing Kart’s Structural Frame Colliding against a Rigid Wall,” 2023, doi: 10.18419/DARUS-3789.
    3. 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.
    4. 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.

  3. 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, Art. no. 20, 2022, doi: 10.1016/j.ifacol.2022.09.117.
    2. J. Kneifl, J. Hay, and J. Fehr, “Real-time Human Response Prediction Using a Non-intrusive Data-driven Model Reduction Scheme,” IFAC-PapersOnLine, vol. 55, Art. no. 20, 2022, doi: 10.1016/j.ifacol.2022.09.109.

  4. 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.

Data and software publications PN 7-6 and PN 7-6 (II)

  1. J. Kneifl, D. Rosin, O. Avci, O. Röhrle, and J. C. Fehr, “Continuum-mechanical Forward Simulation Results of a Human Upper-limb Model Under Varying Muscle Activations.” 2023. doi: 10.18419/darus-3302.
  2. J. Kneifl, J. Hay, and J. Fehr, “Human Occupant Motion in Pre-Crash Scenario.” 2022. doi: 10.18419/darus-2471.
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