https://jonaskneifl.com/tag/model-order-reduction/Model Order ReductionWowchemy (https://wowchemy.com)en-usMon, 01 Jun 2020 00:00:00 +0000https://jonaskneifl.com/media/logo_hu03f9a0fa0db54419c4c274e8859d8cd7_28119_300x300_fit_lanczos_3.pnghttps://jonaskneifl.com/tag/model-order-reduction/https://jonaskneifl.com/project/surrogate-modeling/Mon, 01 Jun 2020 00:00:00 +0000https://jonaskneifl.com/project/surrogate-modeling/<p>High-fidelity simulations of crash, structural, and musculoskeletal systems are too slow for design loops, control, or real-time use. My PhD work (University of Stuttgart, SimTech) built data-driven surrogates that learn a low-dimensional latent representation of the full-order solution and model its dynamics there — without access to the simulation code.</p> <p>Highlights include non-intrusive nonlinear model reduction for structural dynamics, multi-hierarchical surrogates combining graph neural networks with mesh simplification, real-time human response prediction, and surrogate-accelerated stability analysis. The work is summarized in my <a href="https://jonaskneifl.com/uploads/dissertation.pdf">dissertation</a>.</p>