Data-driven method to learn the most probable transition pathway and stochastic differential equation
DOI10.1016/j.physd.2022.133559zbMath1504.60107arXiv2111.08944OpenAlexW4306931969WikidataQ115341672 ScholiaQ115341672MaRDI QIDQ2677788
Dongfang Li, Jianyu Hu, Xiaoli Chen, Jin-qiao Duan
Publication date: 6 January 2023
Published in: Physica D (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2111.08944
inverse problemEuler-Lagrange equationphysics-informed neural networksMarkovian bridge processmost probable transition pathway
Artificial neural networks and deep learning (68T07) Stochastic ordinary differential equations (aspects of stochastic analysis) (60H10) Computational methods for stochastic equations (aspects of stochastic analysis) (60H35) Numerical solutions to stochastic differential and integral equations (65C30) Lagrange's equations (70H03)
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