Abstract Design optimization of structural and multidisciplinary systems under uncertainty
has been an active area of research due to its evident advantages over deterministic design …

Bayesian optimization is a metamodel-based global optimization approach that can balance
between exploration and exploitation. It has been widely used to solve single-objective …

The dynamics of a self-propelled capsule robot for small-bowel endoscopy driven by its
internal vibro-impact excitation is studied in this paper. Due to its complex anatomy, the …

Time-dependent reliability-based design optimization is an effective tool to guarantee a high
reliability of the product during the full life cycle. However, the necessarily repeated …

Time-variant reliability analysis (TRA) is widely utilized to assess the performance of
engineering structures under various time-variant uncertainties. Recently, the time …

In recent years, multi-agent deep reinforcement learning has progressed rapidly as reflected
by its increasing adoptions in industrial applications. This paper proposes a Guided …

The purpose of this paper is to present an approach for multi-objective reliability-based
seismic design optimization of Steel Moment Resisting Frames (SMRFs), considering weight …

Purpose During the running of automobile, the stabilizer bar is frequently subjected to the
impact of complex random loads, which is prone to fatigue failure and accident. In regard to …

Abstract A novel Bayesian-Entropy Gaussian Process (BEGP) is proposed for constrained
metamodeling. Gaussian Process (GP) regression is a flexible and robust tool for surrogate …

Abstract The Gaussian Process (GP) model has become one of the most popular methods
and exhibits superior performance among surrogate models in many engineering design …