AI in the Sciences and Engineering

Abstract

AI is having a profound impact on science by accelerating discoveries across physics, chemistry, biology, and engineering. This course aims to present a highly topical selection of AI applications across these fields. Emphasis will be placed on using AI, particularly deep learning, to understand systems modelled by PDEs, and key scientific machine learning concepts and themes will be discussed.

Objective

Learning objectives: - Aware of advanced applications of AI in the sciences and engineering - Familiar with the design, implementation, and theory of these algorithms - Understand the pros/cons of using AI and deep learning for science - Understand key scientific machine learning concepts and themes

Content

A selection of the following topics will be presented in the lectures: 1. Key scientific tasks common to many scientific domains, such as simulation, inverse problems, equation discovery, design, and control problems, and issues with traditional methods for solving them 2. Physics-informed neural networks for solving forward, inverse and equation discovery problems related to PDEs 3. Neural operators, including Fourier neural operators and DeepONets, for learning efficient surrogate models, and their theoretical foundations 4. Differentiable scientific algorithms, neural differential equations, and the benefits of hybrid workflows 5. AI for symbolic regression and equation discovery 6. Applications of graph neural networks in science 7. Guest lectures on AI for chemistry and biology 8. Large language models and other Foundation models for scientific discovery Applications using these techniques will be illustrated across fluid dynamics, wave physics, medical physics, molecular design, and computational biology. Several examples where AI algorithms outperform traditional scientific workflows will be shown.

Resources