Learning Reversible Symplectic Dynamics
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
Text
Valperga et al. "Learning Reversible Symplectic Dynamics." Proceedings of The 4th Annual Learning for Dynamics and Control Conference, 2022.Markdown
[Valperga et al. "Learning Reversible Symplectic Dynamics." Proceedings of The 4th Annual Learning for Dynamics and Control Conference, 2022.](https://mlanthology.org/l4dc/2022/valperga2022l4dc-learning/)BibTeX
@inproceedings{valperga2022l4dc-learning,
title = {{Learning Reversible Symplectic Dynamics}},
author = {Valperga, Riccardo and Webster, Kevin and Turaev, Dmitry and Klein, Victoria and Lamb, Jeroen},
booktitle = {Proceedings of The 4th Annual Learning for Dynamics and Control Conference},
year = {2022},
pages = {906-916},
volume = {168},
url = {https://mlanthology.org/l4dc/2022/valperga2022l4dc-learning/}
}