Journal article
Port-Hamiltonian neural networks for learning explicit time-dependent dynamical systems
- Abstract:
-
Accurately learning the temporal behavior of dynamical systems requires models with well-chosen learning biases. Recent innovations embed the Hamiltonian and Lagrangian formalisms into neural networks and demonstrate a significant improvement over other approaches in predicting trajectories of physical systems. These methods generally tackle autonomous systems that depend implicitly on time or systems for which a control signal is known a priori. Despite this success, many real world dynamica...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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Authors
Funding
Rhodes Trust
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Bibliographic Details
- Publisher:
- American Physical Society Publisher's website
- Journal:
- Physical Review E Journal website
- Volume:
- 104
- Issue:
- 3
- Article number:
- 034312
- Place of publication:
- United States
- Publication date:
- 2021-09-29
- Acceptance date:
- 2021-09-14
- DOI:
- EISSN:
-
2470-0053
- ISSN:
-
2470-0045
- Pmid:
-
34654178
Item Description
- Language:
- English
- Keywords:
- Pubs id:
-
1203165
- Local pid:
- pubs:1203165
- Deposit date:
- 2023-01-20
Terms of use
- Copyright holder:
- American Physical Society
- Copyright date:
- 2021
- Rights statement:
- © 2021 American Physical Society
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