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[01151] Structure-Preserving Neural Networks for Hamiltonian Systems

  • Session Time & Room : 3D (Aug.23, 15:30-17:10) @E606
  • Type : Contributed Talk
  • Abstract : When solving Hamiltonian systems using numerical integrators, preserving the symplectic structure is crucial. We analyze whether the same is true if neural networks (NN) are used. In order to include the symplectic structure in the NN's topology we formulate a generalized framework for two well-known NN topologies and discover a novel topology outperforming all others. We find that symplectic NNs generalize better and give more accurate long-term predictions than physics-unaware NNs.
  • Classification : 65Lxx, 68T07, 85-08
  • Format : Talk at Waseda University
  • Author(s) :
    • Philipp Horn (Eindhoven University of Technology)
    • Barry Koren (Eindhoven University of Technology)
    • Veronica Saz Ulibarrena (Leiden University)
    • Simon Portegies Zwart (Leiden University)