Work

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

Year: 2023

Type: preprint

Abstract: In this paper, we present large deviation theory that characterizes the exponential estimate for rare events of stochastic dynamical systems in the limit of weak noise. We aim to consider next-to-leading-order approximation for more accurate calculation of mean exit time via computing large deviation prefactors with the research efforts 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 vector field. We corroborate the higher effectiveness and accuracy of our algorithm with a practical example. Numerical experiments demonstrate its powerful function in exploring internal mechanism of rare events triggered by weak random fluctuations.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2306.11418

Authors: Li, Yang, Yuan, Shenglan, Lu, Linghongzhi, Liu, Xianbin

Institutions: Nanjing University of Science and Technology, Huazhong University of Science and Technology, Nanjing University of Aeronautics and Astronautics

Language: English

Topic: stochastic dynamics and bifurcation

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

Funders: National Natural Science Foundation of China, Government of Jiangsu Province, Natural Science Foundation of Jiangsu Province

Awards: G3586921859, G3984338337

OpenAlex ID: W4381586557

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