一种基于机器学习的重要抽样方法计算稀有事件概率
A Machine-Learning-Based Importance Sampling Method to Compute Rare Event Probabilities
摘要：
我们开发了一种新的计算方法来评估所产生的随机初始化的非线性动力系统的极端漂移概率。该方法使用偏移概率理论，制定了一系列的贝叶斯逆问题，当解决，产生的偏置分布。解决多个贝叶斯逆问题可能是昂贵的;在更高的维度上更是如此。为了减轻计算成本，我们建立了基于机器学习的代理来解决贝叶斯逆问题，从而产生偏置分布。然后，可以在重要性抽样过程中使用该偏置分布来估计极端偏移概率。
英文摘要：
We develop a novel computational method for evaluating the extreme excursion probabilities arising from random initialization of nonlinear dynamical systems. The method uses excursion probability theory to formulate a sequence of Bayesian inverse problems that, when solved, yields the biasing distribution. Solving multiple Bayesian inverse problems can be expensive; more so in higher dimensions. To alleviate the computational cost, we build machine-learning-based surrogates to solve the Bayesian inverse problems that give rise to the biasing distribution. This biasing distribution can then be used in an importance sampling procedure to estimate the extreme excursion probabilities.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
