Progress in combustion science and engineering has led to the generation of large amounts
of data from large-scale simulations, high-resolution experiments, and sensors. This corpus …

Many properties of nanoparticles are governed by their shape, size, polydispersity and
surface chemistry. To apply nanoparticles in chemical sensing, medical diagnostics …

Traffic prediction plays a crucial role in alleviating traffic congestion which represents a
critical problem globally, resulting in negative consequences such as lost hours of additional …

Bayesian optimization has emerged at the forefront of expensive black-box optimization due
to its data efficiency. Recent years have witnessed a proliferation of studies on the …

Two-dimensional (2D) material research is rapidly evolving to broaden the spectrum of
emergent 2D systems. Here, we review recent advances in the theory, synthesis …

Abstract Machine learning (ML) methods have become an important tool for modelling and
forecasting complex high-dimensional spatiotemporal datasets such as those found in …

This tutorial aims to provide an intuitive introduction to Gaussian process regression (GPR).
GPR models have been widely used in machine learning applications due to their …

Metamaterials are artificial materials designed to exhibit effective material parameters that
go beyond those found in nature. Composed of unit cells with rich designability that are …

On top of machine learning (ML) models, uncertainty quantification (UQ) functions as an
essential layer of safety assurance that could lead to more principled decision making by …

The Gaussian process is an indispensable tool for spatial data analysts. The onset of the
“big data” era, however, has lead to the traditional Gaussian process being computationally …