Yang Li , Feng Zhao , Jianlong Wang , Shengyuan Xu
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引用次数: 0
Abstract
Rare events induced by random perturbations are ubiquitous phenomena in natural systems, where the exit location distribution is a significant quantity, and its computation is challenging. In this study, we compute the exit location distribution of stochastic dynamical systems with weak Gaussian noise for a noncharacteristic boundary based on deep learning and large deviation theory. First, we introduce the perturbation expressions of the prefactor and exit location distribution via Wentzel–Kramers–Brillouin approximation. We then design a deep learning method to compute the quasipotential, the prefactor, and the exit location distribution. Two examples are described to verify the effectiveness of the proposed algorithm. The findings of this study are expected to provide valuable insights into exploring the mechanisms of rare events triggered by random fluctuations.
期刊介绍:
This journal provides a forum for scholarly work dealing primarily with probabilistic and statistical approaches to contemporary solid/structural and fluid mechanics problems encountered in diverse technical disciplines such as aerospace, civil, marine, mechanical, and nuclear engineering. The journal aims to maintain a healthy balance between general solution techniques and problem-specific results, encouraging a fruitful exchange of ideas among disparate engineering specialities.