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.
Data-driven Effective Modeling of Multiscale Stochastic Dynamical Systems
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abstract
We present a numerical method for learning the dynamics of slow components of unknown multiscale stochastic dynamical systems. While the governing equations of the systems are unknown, bursts of observation data of the slow variables are available. By utilizing the observation data, our proposed method is capable of constructing a generative stochastic model that can accurately capture the effective dynamics of the slow variables in distribution. We present a comprehensive set of numerical examples to demonstrate the performance of the proposed method.
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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.