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@inproceedings{Valperga:2022,
author = {Valperga, R and Webster, K and Klein, V and Turaev, D and Lamb, JSW},
publisher = {PLMR},
title = {Learning reversible symplectic dynamics},
url = {http://arxiv.org/abs/2204.12323v1},
year = {2022}
}

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TY  - CPAPER
AB  - Time-reversal symmetry arises naturally as a structural property in manydynamical systems of interest. While the importance of hard-wiring symmetry isincreasingly recognized in machine learning, to date this has eludedtime-reversibility. In this paper we propose a new neural network architecturefor learning time-reversible dynamical systems from data. We focus inparticular on an adaptation to symplectic systems, because of their importancein physics-informed learning.
AU  - Valperga,R
AU  - Webster,K
AU  - Klein,V
AU  - Turaev,D
AU  - Lamb,JSW
PB  - PLMR
PY  - 2022///
TI  - Learning reversible symplectic dynamics
UR  - http://arxiv.org/abs/2204.12323v1
UR  - https://proceedings.mlr.press/v168/valperga22a.html
UR  - http://hdl.handle.net/10044/1/97319
ER  -

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ProfessorJeroenLamb

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