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A Feynman-Kac formula approach for computing expectations and threshold crossing probabilities of non-smooth stochastic dynamical systems

机译:一种用于计算期望和阈值交叉概率的Feynman-Kac配方方法,非平滑随机动力系统的预期和阈值交叉概率

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We present a computational alternative to probabilistic simulations for non-smooth stochastic dynamical systems that are prevalent in engineering mechanics. As examples, we target (1) stochastic elasto-plastic problems, which involve transitions between elastic and plastic states, and (2) obstacle problems with noise, which involve discrete impulses due to collisions with an obstacle. We formally introduce a class of partial differential equations related to the Feynman-Kac formula, where the underlying stochastic processes satisfy variational inequalities modeling elasto-plastic and obstacle oscillators. We then focus on solving them numerically The main challenge in solving these equations is the non-standard boundary conditions which describe the behavior of the underlying process on the boundary. We illustrate how to use our approach to compute expectations and other statistical quantities, such as the asymptotic growth rate of variance in asymptotic formulae for threshold crossing probabilities. (C) 2019 Elsevier B.V. All rights reserved.
机译:我们为工程力学中普遍存在的非平滑随机动力系统提供了一种计算替代方法。作为示例,我们靶向(1)随机弹塑性问题,涉及弹性和塑料状态之间的过渡,(2)噪音障碍物问题,这涉及由于障碍物的碰撞而导致离散的冲动。我们正式地介绍了一类与Feynman-KAC公式相关的部分微分方程,其中底层随机过程满足了弹性塑料和障碍振荡器的变分不等式。然后,我们专注于在数值上解决它们的主要挑战是解决这些方程的主要挑战是描述基础过程在边界上的行为的非标准边界条件。我们说明了如何利用我们的方法来计算期望和其他统计量,例如渐近式渐近的差异差异的渐近生长速率,用于阈值交叉概率。 (c)2019年Elsevier B.V.保留所有权利。

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