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Machine Learning Conservation Laws from Trajectories

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PhysRevLett.126.180604.pdf

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sword-2022-05-02T16:31:22.original.xml (130 B)
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Author(s)
Liu, Ziming
•
Tegmark, Max
Date Issued
2021
Journal
Physical Review Letters
Publisher
American Physical Society (APS)
Citation
Liu, Ziming and Tegmark, Max. 2021. "Machine Learning Conservation Laws from Trajectories." Physical Review Letters, 126 (18).
Version
Final published version
Abstract
We present AI Poincar\'e, a machine learning algorithm for auto-discovering conserved quantities using trajectory data from unknown dynamical systems. We test it on five Hamiltonian systems, including the gravitational 3-body problem, and find that it discovers not only all exactly conserved quantities, but also periodic orbits, phase transitions and breakdown timescales for approximate conservation laws.
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
https://hdl.handle.net/1721.1/142231
DOI of Published Version
10.1103/PHYSREVLETT.126.180604
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