Deep Gaussian Process model and its application in active learning reliability analysis

Abstract

Reliability analysis of engineering machinery faces significant challenges due to highdimensional input spaces and scarce failure samples, making accurate reliability assessment difficult. Recognizing the potential of Deep Gaussian Processes in this domain, this paper proposes a surrogate modeling method integrating Deep Gaussian Processes with active learning strategies and develops a dedicated toolbox. The toolbox employs a modular architecture to enable scalable layer-wise operations and includes computational efficiency benchmarks. Through two structural reliability case studies, including a numerical example with explicit limit states and a practical engineering application requiring finite element analysis, the proposed method demonstrates superiority over traditional surrogate models in terms of sample efficiency and prediction accuracy. The findings highlight the practical utility of Deep Gaussian Processes in advancing reliability engineering practices.