Data-driven method to learn the most probable transition pathway and stochastic differential equation-Reference-Cited by-同舟云学术

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Data-driven method to learn the most probable transition pathway and stochastic differential equation

Published:2023-01 Issue: Volume:443 Page:133559
ISSN:0167-2789
Container-title:Physica D: Nonlinear Phenomena
language:en
Short-container-title:Physica D: Nonlinear Phenomena

Author:

Chen Xiaoli^ORCID,Duan Jinqiao,Hu Jianyu,Li Dongfang

Publisher

Elsevier BV

Subject

Condensed Matter Physics,Statistical and Nonlinear Physics

Reference52 articles.

1. Random Dynamical Systems;Arnold,2003

2. An Introduction to Stochastic Dynamics;Duan,2015

3. Effective Dynamics of Stochastic Partial Differential Equations;Duan,2014

4. Stochastic Climate Models, Vol. 49;Imkeller,2001

5. Analysis and Approximation of Rare Events: Representations and Weak Convergence Methods;Budhiraja,2019

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3. Deep reinforcement learning in finite-horizon to explore the most probable transition pathway;Physica D: Nonlinear Phenomena;2024-02

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