Learning Symmetries of Classical Integrable Systems
The solution of problems in physics is often facilitated by a change of variables. In this work we present neural transformations to learn symmetries of Hamiltonian mechanical systems. Maintaining the Hamiltonian structure requires novel network architectures that parametrize symplectic transformations. We demonstrate the utility of these architectures by learning the structure of integrable models. Our work exemplifies the adaptation of neural transformations to a family constrained by more than the condition of invertibility, which we expect to be a common feature of applications of these methods.
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Roberto Bondesan (add twitter)
Austen Lamacraft (edit)
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06/11/19 06:02PM
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arxiv_in_review: #ICML2019 Learning Symmetries of Classical Integrable Systems. (arXiv:1906.04645v1 [physics.comp-ph]) https://t.co/GfmadrwXci
Memoirs: Learning Symmetries of Classical Integrable Systems. https://t.co/h56mwA3awq
AustenLamacraft: Can a neural network learn the right variables for a physics problem? New work on learning symmetries in classical Hamiltonian mechanics with Roberto Bondesan. Normalizing flows need new architectures to deal with symplectic transformations! https://t.co/BcClTAwWiz
arxiv_cs_LG: Learning Symmetries of Classical Integrable Systems. Roberto Bondesan and Austen Lamacraft https://t.co/uSq4SsevwN
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