Related papers: Learning reversible symplectic dynamics

Learning reversible symplectic dynamics

URL: http://arxiv.org/abs/2204.12323v1
Date: Tue, 26 Apr 2022 14:07:40 GMT
Title: Learning reversible symplectic dynamics
Authors: Riccardo Valperga, Kevin Webster, Victoria Klein, Dmitry Turaev and Jeroen S. W. Lamb
Abstract summary: We propose a new neural network architecture for learning time-reversible dynamical systems from data. We focus on an adaptation to symplectic systems, because of their importance in physics-informed learning.
Score: 0.0
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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.

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