Learning reversible symplectic dynamics
Machine Learning
2022-04-28 v1 Dynamical Systems
Computational Physics
Abstract
Time-reversal symmetry arises naturally as a structural property in many dynamical systems of interest. While the importance of hard-wiring symmetry is increasingly recognized in machine learning, to date this has eluded time-reversibility. In this paper we propose a new neural network architecture for learning time-reversible dynamical systems from data. We focus in particular on an adaptation to symplectic systems, because of their importance in physics-informed learning.
Cite
@article{arxiv.2204.12323,
title = {Learning reversible symplectic dynamics},
author = {Riccardo Valperga and Kevin Webster and Victoria Klein and Dmitry Turaev and Jeroen S. W. Lamb},
journal= {arXiv preprint arXiv:2204.12323},
year = {2022}
}
Comments
Published at the 4th Annual Learning for Dynamics & Control Conference