Journal article
Variational integrator graph networks for learning energy-conserving dynamical systems
- Abstract:
-
Recent advances show that neural networks embedded with physics-informed priors significantly outperform vanilla neural networks in learning and predicting the long-term dynamics of complex physical systems from noisy data. Despite this success, there has only been a limited study on how to optimally combine physics priors to improve predictive performance. To tackle this problem we unpack and generalize recent innovations into individual inductive bias segments. As such, we are able to syste...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Version of record, pdf, 473.2KB)
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- Publisher copy:
- 10.1103/physreve.104.035310
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:
- 035310
- Place of publication:
- United States
- Publication date:
- 2021-09-30
- Acceptance date:
- 2021-09-15
- DOI:
- EISSN:
-
2470-0053
- ISSN:
-
2470-0045
- Pmid:
-
34654151
Item Description
- Language:
- English
- Keywords:
- Pubs id:
-
1203164
- Local pid:
- pubs:1203164
- 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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