Adaptive learning for reliability analysis using Support Vector Machines

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.

Automated summary

This work demonstrates how to approximate the failure probability of an expensive computational model with reliability requirements using Support Vector Machines. An algorithm is proposed to select informative parameter points to improve the approximation accuracy iteratively. Additionally, a method is provided to quantify the uncertainty in the Limit State Function and estimate an upper bound to the failure probability using geometrical arguments.