Abstract: Available Gaussian processes (GPs) are inappropriate when used to globally emulate and optimize nondifferentiable functions with jump discontinuities. This is a substantial limitation as many contemporary engineering ...

Abstract: We enhance the Bayesian optimization (BO) approach for simulation-based design of engineering systems consisting of multiple interconnected expensive simulation models. The goal is to find the global optimum design with ...

Abstract: Prognosis health management is an effective way to improve the operational safety and economy of industrial equipment. The development of an accurate and quick response model to monitor equipment health status, predict ...

Abstract: Inspired by the recent achievements of machine learning in diverse domains, data-driven metamaterials design has emerged as a compelling paradigm that can unlock the potential of multiscale architectures. The model-centric ...

Abstract: The cost of adaptive sampling for global metamodeling depends on the total number of costly function evaluations and to which degree these evaluations are performed in parallel. Conventionally, samples are taken through a ...

Abstract: In this study, we propose a scalable batch sampling scheme for optimization of simulation models with spatially varying noise. The proposed scheme has two primary advantages: (i) reduced simulation cost by recommending ...