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Discovering symmetry invariants and conserved quantities by interpreting siamese neural networks

  • Published:2020-09-25 Issue:3 Volume:2 Page:
  • ISSN:2643-1564
  • Container-title:Physical Review Research
  • language:en
  • Short-container-title:Phys. Rev. Research

Author:

Wetzel Sebastian J.,Melko Roger G.,Scott Joseph,Panju MaysumORCID,Ganesh VijayORCID

Funder

Canada Research Chairs

National Research Council Canada

Natural Sciences and Engineering Research Council of Canada

Innovation, Science and Economic Development Canada

Publisher

American Physical Society (APS)

Subject

General Engineering

Cited by 45 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Bayesian framework for discovering interpretable Lagrangian of dynamical systems from data;Mechanical Systems and Signal Processing;2024-12

2. Learning quantum symmetries with interactive quantum-classical variational algorithms;Journal of Physics A: Mathematical and Theoretical;2024-07-18

3. The R-mAtrIx Net;Machine Learning: Science and Technology;2024-07-04

4. Beyond dynamics: learning to discover conservation principles;Machine Learning: Science and Technology;2024-05-31

5. Learning Conservation Laws in Unknown Quantum Dynamics;PRX Quantum;2024-03-22
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