A generalized framework of neural networks for Hamiltonian systems

This publication has 10 references indexed in Scilit:

• Fast neural Poincaré maps for toroidal magnetic fields
  Plasma Physics and Controlled Fusion, 2020
• SympNets: Intrinsic structure-preserving symplectic networks for identifying Hamiltonian systems
  Neural Networks, 2020
• Newton versus the machine: solving the chaotic three-body problem using deep neural networks
  Monthly Notices of the Royal Astronomical Society, 2020
• Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
  Journal of Computational Physics, 2019
• Stable architectures for deep neural networks
  Inverse Problems, 2017
• A Proposal on Machine Learning via Dynamical Systems
  Communications in Mathematics and Statistics, 2017
• On the reliability of N-body simulations
  Computational Astrophysics and Cosmology, 2015
• Polynomial approximations of symplectic dynamics and richness of chaos in non-hyperbolic area-preserving maps
  Nonlinearity, 2002
• Approximation capabilities of multilayer feedforward networks
  Neural Networks, 1991
• Construction of higher order symplectic integrators
  Physics Letters A, 1990