Learning Stochastic Dynamics from Data

Igor Cialenco; Ming Zhong; Ziheng Guo

Learning Stochastic Dynamics from Data

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arxiv 2403.02595 v1 pith:GZAZDZ26 submitted 2024-03-05 math.NA cs.NA

Learning Stochastic Dynamics from Data

Ziheng Guo , Igor Cialenco , Ming Zhong This is my paper

classification math.NA cs.NA

keywords noisemethodlearningstochastictrajectoryvariousalgorithmcase

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We present a noise guided trajectory based system identification method for inferring the dynamical structure from observation generated by stochastic differential equations. Our method can handle various kinds of noise, including the case when the the components of the noise is correlated. Our method can also learn both the noise level and drift term together from trajectory. We present various numerical tests for showcasing the superior performance of our learning algorithm.

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Cited by 1 Pith paper

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A Weak Penalty Neural ODE for Learning Chaotic Dynamics from Noisy Time Series
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The Weak Penalty Neural ODE uses a weak form loss to filter noise and learn stable chaotic dynamics from noisy observations.