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AI in the Sciences and Engineering
Last Updated: 2026-06-03 00:07:37
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 state of the art AI applications across these fields. Emphasis will be placed on using AI, particularly deep learning, to understand physical and engineering systems, mathematically modelled by PDEs.
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 and engineering. - Understand key scientific machine learning concepts and themes
Content
A selection of the following topics will be presented in the lectures: 1. Introduction to Physics modelled by PDEs and drawbacks of phyiscs-based simulators which provide the rationale for the applications of state-of-the-art AI techniques in this context. 2. Neural PDE solvers, in particular Physics-informed neural networks and their variants. 3. Neural operators: FNO, CNO and Operator Transformers. 4. Graph Neural Networks and Flexible Transformer frameworks for processing data on domains with complex geometries. 5. Generative AI, in particular Diffusion and Flow models, for Multiscale problems and uncertainty quantification. 6. Introduction to Physics Foundation Models. 7. Downstream Applications: UQ, Inverse Problems and Design. AI for Weather and Climate. 8. AI in Chemistry and Biology: Illustrative examples of Graph Neural Networks and Generative AI for Structure based Drug Design.
Resources
Lecture Notes
Lecture slides, recordings, and tutorials will be available on Moodle.
Literature
All the material in the course is based on research articles written in last 1-3 years. The relevant references will be provided.
General Information
- Language
- English
- Levels
- DR , MSC
- Frequency
- Yearly recurring
Examination
- Type
- graded semester performance
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture | AI in the Sciences and Engineering | No time listed | 2 h weekly |
| exercise | AI in the Sciences and Engineering | No time listed | 2 h weekly |
Offered In
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Core Courses (In the ‘core courses’ subcategory, at least two course units must be successfully completed. Only one of the two course units 263-5210-00L Probabilistic Artificial Intelligence resp. 252-0535-00L Advanced Machine Learning may be recognised for credits as a core course. However, the other course unit may be recognised for a different category.)
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Application Area (Only necessary and eligible for the Master degree in Applied Mathematics. One of the application areas specified must be selected for the category Application Area for the Master degree in Applied Mathematics. At least 8 credits are required in the chosen application area. Credits from other application areas cannot be recognised for further application areas.)
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Electives (For the Master's degree in Applied Mathematics the following additional condition (not manifest in myStudies) must be obeyed: At least 14 of the required 26 credits from core courses and electives must be acquired in areas of applied mathematics and further application-oriented fields.)
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Statistics Master (The following courses belong to the curriculum of the Master's Programme in Statistics. The corresponding credits do not count as external credits even for course units where an enrolment at ETH Zurich is not possible.)
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Doctorate Mathematics (More Information at: )
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Subject Specialisation (The list of courses eligible for doctoral students is published each semester in the newsletter of the ZGSM.)
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Graduate School (Official website of the Zurich Graduate School in Mathematics: )
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