5B1 – Applied AI & ML and Advanced Computing

This module explores the application of Artificial Intelligence (AI) in the design and optimisation of next-generation electronic and photonic devices and systems. Students will gain insight into how AI-driven methods can accelerate innovation by enabling more efficient simulations, smarter design workflows, and optimised system performance across a wide range of technologies.

Core Topics

• AI Techniques for Physical Modelling: Introduction to advanced AI frameworks such as Generative Adversarial Networks (GANs) and Physics-Informed Neural Networks (PINNs) for accurate and efficient simulation of complex physical systems.
• AI-Enhanced Design and Characterisation: Exploration of how AI can improve the design and analysis process of electronic and photonic devices, enabling faster convergence to Pareto-optimised solutions.
• Advanced Computing Methods: Study of neuromorphic and optical computing architectures that enable faster, energy-efficient learning and decision-making in intelligent systems.

Practical Design and Simulation
Students will complete simulation-based coursework to apply AI methods in realistic design and optimisation scenarios. Through these exercises, they will gain practical experience in using AI tools to model, analyse, and enhance device and system performance.

Learning Outcomes
By the end of this module, you will:

• Understand key AI techniques relevant to electronic and photonic design.
• Be able to apply AI models for simulation and optimisation of complex systems.
• Recognise how advanced computing methods accelerate learning and decision-making.
• Gain practical skills in AI-assisted simulation and design workflows.