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• W4310731246 abstract "We introduce a machine-learning framework named statistics-informed neural network (SINN) for learning stochastic dynamics from data. This new architecture was theoretically inspired by a universal approximation theorem for stochastic systems, which we introduce in this paper, and the projection-operator formalism for stochastic modeling. We devise mechanisms for training the neural network model to reproduce the correct statistical behavior of a target stochastic process. Numerical simulation results demonstrate that a well-trained SINN can reliably approximate both Markovian and non-Markovian stochastic dynamics. We demonstrate the applicability of SINN to coarse-graining problems and the modeling of transition dynamics. Furthermore, we show that the obtained reduced-order model can be trained on temporally coarse-grained data and hence is well suited for rare-event simulations." @default.
• W4310731246 created "2022-12-16" @default.
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• W4310731246 date "2023-02-01" @default.
• W4310731246 modified "2023-10-16" @default.
• W4310731246 title "Learning stochastic dynamics with statistics-informed neural network" @default.
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• W4310731246 doi "https://doi.org/10.1016/j.jcp.2022.111819" @default.
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