Bayesian optimization is used in many areas of AI for the optimization of black-box
processes and has achieved impressive improvements of the state of the art for a lot of …

There has been a surge in interest in data-driven experimental design with applications to
chemical engineering and drug manufacturing. Bayesian optimization (BO) has proven to be …

Objective: To determine the completeness of argumentative steps necessary to conclude
effectiveness of an algorithm in a sample of current ML/AI supervised learning literature …

We consider the problem of optimizing initial conditions and timing in dynamical systems
governed by unknown ordinary differential equations (ODEs), where evaluating different …

We report the development and application of a scalable machine learning optimisation
framework for batched multi-objective reaction optimisation. Through experimental data …

Most existing black-box optimization methods assume that all variables in the system being
optimized have equal cost and can change freely at each iteration. However, in many real …

The Center for Advanced Mathematics for Energy Research Applications (CAMERA) is an
integrated, cross-disciplinary center aimed at inventing, developing, and delivering the …

Bayesian optimization (BO) is an effective approach for optimizing expensive black-box
functions via potentially noisy function evaluations. However, few BO techniques address …

This study improved an algorithm for optimising costly black-box systems called Bayesian
optimisation. It enhanced surrogate models Bayesian optimisation uses to select samples. It …

This thesis has sought to show how multi-objective Bayesian optimisation can be extended
and applied to a variety of real-world problems. To this end, we have identified several …