Bayesian optimisation is a statistical method that efficiently models and optimises expensive
“black-box” functions. This review considers the application of Bayesian optimisation to …

This review aims to exploit a study on different benchmark test functions used to evaluate the
performance of Meta-Heuristic (MH) optimization techniques. The performance of the MH …

The vast quantity of information brought by big data as well as the evolving computer
hardware encourages success stories in the machine learning community. In the …

This review article highlights state-of-the-art data-driven techniques to discover, encode,
surrogate, or emulate constitutive laws that describe the path-independent and path …

Metamodels aim to approximate characteristics of functions or systems from the knowledge
extracted on only a finite number of samples. In recent years kriging has emerged as a …

Abstract The use of Machine Learning (ML) has rapidly spread across several fields of
applied sciences, having encountered many applications in Structural Dynamics and …

X-ray absorption near-edge structure (XANES) spectra are the fingerprint of the local atomic
and electronic structures around the absorbing atom. However, the quantitative analysis of …

Multi-output regression problems have extensively arisen in modern engineering
community. This article investigates the state-of-the-art multi-output Gaussian processes …

Meta-heuristic algorithms, which require a large number of fitness evaluations before
locating the global optimum, are often prevented from being applied to computationally …

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 …