Abstract: Multifidelity (MF) models abound in simulation-based engineering. Many MF strategies have been proposed to improve the efficiency in engineering processes, especially in design optimization. When it comes to assessing the ...

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: The data-driven approach is emerging as a promising method for the topological design of multiscale structures with greater efficiency. However, existing data-driven methods mostly focus on a single class of microstructures ...

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 ...

Abstract: CrackNet is the result of an 18-month collaboration within a 1-person team to develop a deep learning–based pavement crack detection software that demonstrated successes in terms of consistency for both precision and bias. ...