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Machine Learning Conservation Laws from Trajectories
| dc.contributor.author | Liu, Ziming | |
| dc.contributor.author | Tegmark, Max | |
| dc.date.accessioned | 2022-05-02T16:35:18Z | |
| dc.date.available | 2022-05-02T16:35:18Z | |
| dc.date.issued | 2021 | |
| dc.identifier.uri | https://hdl.handle.net/1721.1/142231 | |
| dc.description.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. | en_US |
| dc.language.iso | en | |
| dc.publisher | American Physical Society (APS) | en_US |
| dc.relation.isversionof | 10.1103/PHYSREVLETT.126.180604 | en_US |
| dc.rights | 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. | en_US |
| dc.source | APS | en_US |
| dc.title | Machine Learning Conservation Laws from Trajectories | en_US |
| dc.type | Article | en_US |
| dc.identifier.citation | Liu, Ziming and Tegmark, Max. 2021. "Machine Learning Conservation Laws from Trajectories." Physical Review Letters, 126 (18). | |
| dc.relation.journal | Physical Review Letters | en_US |
| dc.eprint.version | Final published version | en_US |
| dc.type.uri | http://purl.org/eprint/type/JournalArticle | en_US |
| eprint.status | http://purl.org/eprint/status/PeerReviewed | en_US |
| dc.date.updated | 2022-05-02T16:31:22Z | |
| dspace.orderedauthors | Liu, Z; Tegmark, M | en_US |
| dspace.date.submission | 2022-05-02T16:31:25Z | |
| mit.journal.volume | 126 | en_US |
| mit.journal.issue | 18 | en_US |
| mit.license | PUBLISHER_POLICY | |
| mit.metadata.status | Authority Work and Publication Information Needed | en_US |
