Failure-averse Active Learning for Physics-constrained Systems

Abstract

Active learning is a subfield of machine learning that is devised for 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: the safe variance reduction explores the safe region to reduce the variance of the target model, and the safe region expansion aims to extend the explorable region exploiting the probabilistic model of constraints. The global acquisition function is devised to judiciously optimize acquisition functions of two tasks, and its theoretical properties are provided. 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.

12 pages

References in corpus (11)

• Sequential design of computer experiments for the estimation of a probability of failure
• Bayesian Optimization with Unknown Constraints
• Predictive Entropy Search for Bayesian Optimization with Unknown Constraints
• Active learning of deep surrogates for PDEs: Application to metasurface design
• Physics-Informed CoKriging: A Gaussian-Process-Regression-Based Multifidelity Method for Data-Model Convergence
• Iterative Surrogate Model Optimization (ISMO): An active learning algorithm for PDE constrained optimization with deep neural networks
• Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems
• Scalable Constrained Bayesian Optimization
• Partitioned Active Learning for Heterogeneous Systems
• Physics-Information-Aided Kriging: Constructing Covariance Functions using Stochastic Simulation Models
• APIK: Active Physics-Informed Kriging Model with Partial Differential Equations

Cited by in corpus (1)

• Partitioned Active Learning for Heterogeneous Systems