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Bibliographic Details
Main Authors:	He, Qing, Cai, Wei
Format:	Preprint
Published:	2024
Subjects:	Numerical Analysis Machine Learning Computational Physics 37J11, 70H15, 68T07
Online Access:	https://arxiv.org/abs/2407.00294
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Table of Contents:

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.