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

The full optimization of the design and operation of instruments whose functioning relies on
the interaction of radiation with matter is a super-human task, due to the large dimensionality …

Soil organic carbon (SOC) is the largest carbon pool in terrestrial ecosystems and plays a
crucial role in mitigating climate change and enhancing soil productivity. Microbial-derived …

Bayesian Optimization (BO), the application of Bayesian function approximation to finding
optima of expensive functions, has exploded in popularity in recent years. In particular, much …

Bayesian optimization (BO) is a powerful paradigm for efficient optimization of black-box
objective functions. High-dimensional BO presents a particular challenge, in part because …

Laser wakefield accelerators promise to revolutionize many areas of accelerator science.
However, one of the greatest challenges to their widespread adoption is the difficulty in …

The Linac coherent light source x-ray free-electron laser is a complex scientific apparatus
which changes configurations multiple times per day, necessitating fast tuning strategies to …

Meta-learning can successfully acquire useful inductive biases from data. Yet, its
generalization properties to unseen learning tasks are poorly understood. Particularly if the …

The global optimization of a high-dimensional black-box function under black-box
constraints is a pervasive task in machine learning, control, and engineering. These …

Accelerator physics relies on numerical algorithms to solve optimization problems in online
accelerator control and tasks such as experimental design and model calibration in …