Corpus ID: 212644628

Lagrangian Neural Networks

@article{Cranmer2020LagrangianNN,
  title={Lagrangian Neural Networks},
  author={Miles D. Cranmer and Sam Greydanus and Stephan Hoyer and Peter W. Battaglia and David N. Spergel and Shirley Ho},
  journal={ArXiv},
  year={2020},
  volume={abs/2003.04630}
}

Miles D. Cranmer, Sam Greydanus, +3 authors Shirley Ho

Published 2020

Computer Science, Mathematics, Physics

ArXiv

Accurate models of the world are built upon notions of its underlying symmetries. In physics, these symmetries correspond to conservation laws, such as for energy and momentum. Yet even though neural network models see increasing use in the physical sciences, they struggle to learn these symmetries. In this paper, we propose Lagrangian Neural Networks (LNNs), which can parameterize arbitrary Lagrangians using neural networks. In contrast to models that learn Hamiltonians, LNNs do not require… CONTINUE READING

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