AI-Driven Experimental Design and Optimisation

Introduction

This group focuses on using AI-based approaches to design experiments and processes, including the optimisation of the resulting products, processes and systems. Our members interests span a number of applications, from the formulation of well-known household products, through to internal combustion engines.

Beyond developing and applying AI for design and optimisation, the group also has interests in incorporating these algorithms into laboratory automation.

Research areas

• Bayesian optimisation
• Closed-loop laboratory automation
• Design of experiments
• Multi-objective optimisation

Aims of the interest group

To act as a hub for those using AI approaches in the design and optimisation of experiments.

Promote interdisciplinary collaborations, including with external partners, to advance optimal planning, automation and optimisation of experiments.

Ensure that the decisions taken in AI-driven design and optimisation of experiments is explainable and trustworthy.

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