Failure-Averse Active Learning for Physics-Constrained Systems

Active learning is a subfield of machine learning that is devised for the design and modeling of systems with highly expensive sampling costs. Industrial and engineering systems are generally subject to physics constraints that may induce fatal failures when they are violated, while such constraints are frequently underestimated in active learning. In this paper, we develop a novel active learning method that avoids failures considering implicit physics constraints that govern the system. The proposed approach is driven by two tasks: safe variance reduction explores the safe region to reduce the variance of the target model, and safe region expansion aims to extend the explorable region. The integrated acquisition function is devised to conflate two tasks and judiciously optimize them. The proposed method is applied to the composite fuselage assembly process with consideration of material failure using the Tsai-Wu criterion, and it is able to achieve zero failure without the knowledge of explicit failure regions. Note to Practitioners—This paper is motivated by engineering systems with implicit physics constraints related to system failures. Implicit physics constraints refer to failure processes in which explicit analytic forms do not exist, so demanding numerical simulations or real experiments are required to check one’s safety. The main objective of this paper is to develop an active learning strategy that safely learns the target process in the system by minimizing failures without preliminary reliability analysis. The proposed method mainly targets real systems whose failure conditions are not thoroughly investigated or uncertain. We applied the proposed method to the predictive modeling of composite fuselage deformation in the aircraft manufacturing process, and it achieved zero failure in sampling by considering the composite failure criterion.