Browsing by Author "Wang, Xing"

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    Failure-Averse Active Learning for Physics-Constrained Systems
    Lee, Cheolhei; Wang, Xing; Wu, Jianguo; Yue, Xiaowei (IEEE, 2022-10)
    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.
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    Physics-Constrained Bayesian Optimization for Optimal Actuators Placement in Composite Structures Assembly
    AlBahar, Areej; Kim, Inyoung; Wang, Xing; Yue, Xiaowei (IEEE, 2022-08)
    Complex constrained global optimization problems such as optimal actuators placement are extremely challenging. Such challenges, including nonlinearity and nonstationarity of engineering response surfaces, hinder the use of ordinary constrained Bayesian optimization (CBO) techniques with standard Gaussian processes as surrogate models. To overcome those challenges, we propose a physics-constrained Bayesian optimization with multi-layer deep structured Gaussian processes, MGP-CBO. Specifically, we introduce a surrogate model with a multi-layer deep Gaussian process (MGP) mean function. The hierarchical structure of our model enables the applicability of constrained Bayesian optimization to complex nonlinear and nonstationary processes. The deep Gaussian process regression model, MGP, can efficiently and effectively represent the response surface function between actuators and dimensional deformations, thus yielding a better estimated global optimum in a shorter computational time. The proposed MGP-CBO model can realize faster convergence to the global optimum with lower constraint violations. Through extensive evaluations carried out on synthetic problems and a real-world engineering design problem, we show that MGP-CBO outperforms existing benchmarks. Although we use the optimal actuators placement as a demonstration example, the proposed MGP-CBO model can be applied to other complex nonstationary engineering optimization problems. Note to Practitioners—Bayesian optimization is a widely used sequential design strategy for engineering optimization because it does not rely on functional forms of response surfaces. This paper helps address two questions in practice: (i) how to incorporate physics constraints into Bayesian optimization. (ii) How to do Bayesian optimization when the systems have hierarchical structures. In practice, the hierarchical system structure is ubiquitous, and the engineering optimization is constrained by physical laws or special requirements. Therefore, the proposed physics-constrained Bayesian optimization with a multi-layer Gaussian process could provide a new tool for engineering design optimization problems. The computational convergence and complexity have been investigated. The proposed method is applicable to broad complex and nonstationary engineering optimization problems.