Bayesian Optimization is a useful tool for experiment design. Unfortunately, the classical,
sequential setting of Bayesian Optimization does not translate well into laboratory …

Abstract Machine learning models are promising as surrogates in optimization when
replacing difficult to solve equations or black-box type models. This work demonstrates the …

Small-angle scattering (SAS) is a widely used characterization technique that provides
structural information in soft materials at varying length scales (nanometers to microns). The …

Batch Bayesian optimisation and Bayesian quadrature have been shown to be sample-
efficient methods of performing optimisation and quadrature where expensive-to-evaluate …

Impactful applications such as materials discovery, hardware design, neural architecture
search, or portfolio optimization require optimizing high-dimensional black-box functions …

Real-world optimisation problems often feature complex combinations of (1) diverse
constraints,(2) discrete and mixed spaces, and are (3) highly parallelisable.(4) There are …

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 …

Offline optimization aims to maximize a black-box objective function with a static dataset and
has wide applications. In addition to the objective function being black-box and expensive to …

Bayesian optimisation is a powerful method for optimising black-box functions, popular in
settings where the true function is expensive to evaluate and no gradient information is …

Dynamical systems analysis and optimization is pivotal for safe, efficient, and resilient
processes that consistently deliver high-quality products. Conventionally, decision-making …