Adaptive learning for reliability analysis using Support Vector Machines
针对计算成本高的系统可靠性模型，提出用支持向量机自适应学习高似然区域来近似失效概率，并通过几何方法量化不确定性、估计失效概率上界。
Given an expensive computational model of a system subject to reliability requirements, this work shows how to approximate the failure probability by learning adaptively the high-likelihood regions of the Limit State Function using Support Vector Machines. To this end, an algorithm is proposed that selects informative parameter points to add to training data at each iteration to improve the accuracy of the approximation. Furthermore, we provide a means to quantify the uncertainty in the Limit State Function, using geometrical arguments to estimate an upper bound to the failure probability.