SGFNN learns a stochastic generating function via an autoencoder from paired state observations, yielding symplectic and more accurate long-term predictions for stochastic Hamiltonian systems than sFML.
Structure-Preserving Algorithms for Ordinary Differential Equations[M]
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Learning Stochastic Hamiltonian Systems via Stochastic Generating Function Neural Network
SGFNN learns a stochastic generating function via an autoencoder from paired state observations, yielding symplectic and more accurate long-term predictions for stochastic Hamiltonian systems than sFML.