Peter Benner - Discovery and Model Reduction of Hamiltonian Systems - IPAM at UCLA
Recorded 14 April 2026. Peter Benner of the Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, presents "Discovery and Model Reduction of Hamiltonian Systems" at IPAM's Learning Models from Data for Multi-Fidelity Fusion Plasma Physics Workshop.Abstract: One often encountered computational issue in studying nuclear fusion reactors is the simulation of the behavior of confined plasmas in a controlled magnetic field. Numerical modeling requires the solution of the Vlasov equation, potentially coupled to other partial differential equations. After spatial discretization, this leads to high-dimensional Hamiltonian systems. Fast simulation under varying conditions requires learning a compact, reduced-order model that is fast to simulate and faithfully reproduces the kinetics of the full-order model. We discuss several approaches to learn Hamiltonian systems from data. Model reduction can be achieved using variational autoencoders. We show how the underlying Hamiltonian structure, resulting in a symplectic flow field, can be preserved using symplectic convolutional neural networks. This talk will discuss joint research with Thomas Bendokat, Konrad Janik, Pawan K. Goyal, Igor Pontes Duff, and Süleyman Yildiz.
Learn more online at: https://www.ipam.ucla.edu/programs/workshops/workshop-ii-learning-models-from-data-for-multi-fidelity-fusion-plasma-physics/ Receive SMS online on sms24.me
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