This repository is the official project page of the course AI in the Sciences and Engineering, ETH Zurich.
Lecturers: Prof. Dr. Siddhartha Mishra, Dr. Ben Moseley
Assistants: Bogdan Raonic, Victor Armegioiu
Course page can be found at this link
Recordings of the lectures can be found at this YouTube link.
A selection of the following topics is presented in the lectures:
- 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
- Physics-informed neural networks for solving forward, inverse and equation discovery problems related to PDEs
- Neural operators, including Fourier neural operators, Convolutional neural operators and DeepONets, for learning efficient surrogate models, and their theoretical foundations
- Differentiable scientific algorithms, neural differential equations, and the benefits of hybrid workflows
- AI for symbolic regression and equation discovery
- Applications of graph neural networks in science
- 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.
- Tutorial 01 - Function Approximation with Pytorch
- Tutorial 02 - Cross Validation and Intro to CNNs
- Tutorial 03 - PINN Training
- Tutorial 04 - PINNs for Inverse Problems
- Tutorial 05 - Operator Learning - Fourier Neural Operator
- Tutorial 06 - Operator Learning - Convolutional Neural Operator
- Tutorial 07 - Introduction to Graph Neural Networks
- Tutorial 08 - Vision Transformers for solving PDEs
- Tutorial 09 - Denoising Diffusion Probabilistic Models - Please visit this link to access the notebook
- Tutorial 10 - Coding Autodiff
- Tutorial 11 - Introduction to JAX, NDEs and diffusion models
- Recordings of the 2023 lectures can be found at this link