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Data-driven Effective Modeling of Multiscale Stochastic Dynamical Systems
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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.
Forward citations
Cited by 2 Pith papers
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Learning Stochastic Hamiltonian Systems via Stochastic Generating Function Neural Network
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A hybrid generative model combining an exit probability neural network with a training-free diffusion model learns stochastic flow maps for SDEs in bounded domains with absorbing boundaries.
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