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Computing large deviation prefactors of stochastic dynamical systems based on machine learning

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We present a large deviation theory that characterizes the exponential estimate for rare events in stochastic dynamical systems in the limit of weak noise.We aim to consider a next-to-leading-order approximation for more accurate calculation of the mean exit time by computing large deviation prefactors with the aid of machine learning.More specifically,we design a neural network framework to compute quasipotential,most probable paths and prefactors based on the orthogonal decomposition of a vector field.We corroborate the higher effectiveness and accuracy of our algorithm with two toy models.Numerical experiments demonstrate its powerful functionality in exploring the internal mechanism of rare events triggered by weak random fluctuations.

machine learninglarge deviation prefactorsstochastic dynamical systemsrare events

李扬、袁胜兰、陆凌宏志、刘先斌

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School of Automation,Nanjing University of Science and Technology,Nanjing 210094,China

Department of Mathematics,School of Sciences,Great Bay University,Dongguan 523000,China

Center for Mathematical Sciences,Huazhong University of Science and Technology,Wuhan 430074,China

State Key Laboratory of Mechanics and Control for Aerospace Structures,College of Aerospace Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China

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江苏省自然科学基金国家自然科学基金国家自然科学基金

BK202209171200121312302035

2024

10.1088/1674-1056/ad12a8

中国物理B(英文版)
中国物理学会和中国科学院物理研究所

中国物理B(英文版)

CSTPCDEI
影响因子：0.995
ISSN：1674-1056
年,卷(期)：2024.33(4)
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