论文标题
一种基于机器学习的重要性抽样方法，用于计算稀有事件概率
A Machine-Learning-Based Importance Sampling Method to Compute Rare Event Probabilities
论文作者
论文摘要
我们开发了一种新型的计算方法，用于评估非线性动力学系统随机初始化引起的极端偏移概率。该方法使用偏移概率理论来制定一系列贝叶斯逆问题，该贝叶斯逆问题在解决时会产生偏见分布。解决多个贝叶斯逆问题可能很昂贵。更高的维度。为了减轻计算成本，我们建立了基于机器学习的替代物来解决贝叶斯的反问题，从而导致偏见分布。然后可以将这种偏见分布用于重要性抽样程序，以估计极端的偏移概率。
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.
