Abstract Machine learning (ML) has been increasingly used to aid aerodynamic shape
optimization (ASO), thanks to the availability of aerodynamic data and continued …

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 …

This paper proposes a system active learning Kriging (SALK) method to handle system
reliability-based design optimization (SRBDO) problems, where responses of all constraints …

Surrogate models have been widely employed as approximations of expensive physics-
based simulations to alleviate the computational burden in reliability-based design …

Surrogate models and adaptive methods can release the huge computational burden of
structural reliability analysis. However, it is very difficult to guarantee the accuracy of …

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 …

Even though many efforts have been devoted to effective strategies to build accurate
surrogate models, surrogate model uncertainty is inevitable due to a limited number of …

In many practical applications, probabilistic and bounded uncertainties often arise
simultaneously, and these uncertainties can be described by using probability and convex …

This article proposes a new method for hybrid reliability‐based design optimization under
random and interval uncertainties (HRBDO‐RI). In this method, Monte Carlo simulation …

Shared autonomous electric vehicles (SAEVs) are a promising car-sharing service expected
to be implemented in the near future. However, existing studies on the optimization of SAEV …