In the last few decades, the influence of machine learning has permeated many areas of
science and technology, including the field of materials science. This toolkit of data driven …

Resided at the intersection of multi-fidelity optimization (MFO) and Bayesian optimization
(BO), MF BO has found a niche in solving expensive engineering design optimization …

To reduce the computational burden of aerodynamic design optimization, a multi-fidelity
expected improvement (MFEI) method is developed, based on the error analysis of a multi …

Culture media used in industrial bioprocessing and the emerging field of cellular agriculture
is difficult to optimize due to the lack of rigorous mathematical models of cell growth and …

In recent years, research on multi-fidelity (MF) surrogate modeling, which integrates high-
fidelity (HF) and low-fidelity (LF) models, has been conducted to improve efficiency in …

This paper presents a novel infill-sampling strategy for adaptive gradient-enhanced Kriging
(AGEK) that delivers superior results on a limited budget. The primary innovation of this …

Multi-fidelity surrogate models fusing data from different fidelity systems can significantly
reduce the computational cost while ensuring the model accuracy. The focus of this paper is …

Bayesian optimization (BO) is a sequential optimization strategy that is increasingly
employed in a wide range of areas such as materials design. In real-world applications …

This paper presents an effective sequence interval and correlation inverse strategy for the
uncertain inverse problem, aiming to identify the uncertainties and non-probabilistic …

Both computational and experimental material discovery bring forth the challenge of
exploring multidimensional and often non-differentiable parameter spaces, such as phase …