Active learning accelerated Monte-Carlo simulation based on the modified K-nearest neighbors algorithm and its application to reliability estimations

Abstract

This paper proposes an active learning accelerated Monte-Carlo simulation method based on the modified K-nearest neighbors algorithm. The core idea of the proposed method is to judge whether or not the output of a random input point can be postulated through a classifier implemented through the modified K-nearest neighbors algorithm. Compared to other active learning methods resorting to experimental designs, the proposed method is characterized by employing Monte-Carlo simulation for sampling inputs and saving a large portion of the actual evaluations of outputs through an accurate classification, which is applicable for most structural reliability estimation problems. Moreover, the validity, efficiency, and accuracy of the proposed method are demonstrated numerically. In addition, the optimal value of K that maximizes the computational efficiency is studied. Finally, the proposed method is applied to the reliability estimation of the carbon fiber reinforced silicon carbide composite specimens subjected to random displacements, which further validates its practicability.

Keywords

• Active learning
• Monte-carlo simulation
• K-nearest neighbors
• Reliability estimation
• Classification