Deep Neural Networks with Symplectic Preservation Properties

Qing He, Wei Cai
6/29/2024
math.NAcs.LGphysics.comp-ph

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Abstract

We propose a deep neural network architecture designed such that its output forms an invertible symplectomorphism of the input. This design draws an analogy to the real-valued non-volume-preserving (real NVP) method used in normalizing flow techniques. Utilizing this neural network type allows for learning tasks on unknown Hamiltonian systems without breaking the inherent symplectic structure of the phase space.

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Cite this paper

@article{he2024deep,
  title  = {Deep Neural Networks with Symplectic Preservation Properties},
  author = {Qing He and Wei Cai},
  year   = {2024},
  eprint = {2407.00294},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2407.00294v1}
}

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