Bayesian optimisation (BO) has been increasingly utilised to guide material discovery. While
BO is advantageous due to its sample efficiency, flexibility and versatility, it is constrained by …

Bayesian optimization (BO) is a powerful technology for optimizing noisy expensive-to-
evaluate black-box functions, with a broad range of real-world applications in science …

Additive manufacturing has enabled the fabrication of advanced reactor geometries,
permitting larger, more complex design spaces. Identifying promising configurations within …

This paper proposes a new hybrid strategy to optimally design membrane separation
problems. We formulate the problem as a Non-Linear Programming (NLP) model. A …

Abstract Hybrid (ie, grey-box) models are a powerful and flexible paradigm for predictive
science and engineering. Grey-box models use data-driven constructs to incorporate …

We propose a new fault diagnosis model for rolling bearings based on a hybrid kernel
support vector machine (SVM) and Bayesian optimization (BO). The model uses discrete …

Optimizing complex reactor geometries is vital to promote enhanced efficiency. We present a
framework to solve this nonlinear, computationally expensive, and derivative-free problem …

This paper provides a tutorial overview of recent advances in learning control policy
representations for complex systems. We focus on control policies that are determined by …

In the last decades, the capacity to generate large amounts of data in science and
engineering applications has been growing steadily. Meanwhile, the progress in machine …

I have been thinking a lot about how machine learning (ML) and related areas (eg, artificial
intelligence, digitalization, and data science) are transforming and will transform our …