Adaptive sequential strategy for risk estimation of engineering systems using Gaussian process regression active learning

摘要

Maximizing the accuracy of the estimated risk, and minimizing the number of calls to the expensive-to-evaluate deterministic model are two major challenges engineers face. Monte Carlo method is the usual method of choice for risk estimation. Since each deterministic run for a complex engineering system may require a significant amount of time, Monte Carlo method may be very time-consuming and impractical. To reduce the computational expense of Monte Carlo method, surrogate models are presented.In this paper, an adaptive sequential strategy based on the Monte Carlo method and Gaussian process regression active learning for risk estimation of engineering systems with minimum computational cost and acceptable accuracy is presented.The proposed adaptive sequential strategy to build designs of experiments is illustrated using a simple One-dimensional explanatory example. Then, the efficiency and accuracy of the presented method are compared with the other available methodologies using several benchmark examples from literature. Finally, the applicability of the presented method for nonlinear and high-dimensional real-world problems are studied.