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AI in the Sciences and Engineering
Last Updated: 2026-02-05 16:38:18
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
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
- MSC
- Frequency
- Yearly recurring
Examination
- Type
- graded semester performance
Registration & Places
- Max Places
- 200
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture | AI in the Sciences and Engineering (formerly: Deep Learning in Scientific Computing) |
|
3 h weekly |
| exercise | AI in the Sciences and Engineering (formerly: Deep Learning in Scientific Computing) |
|
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. Notice regarding 261-5110-00L Optimization for Data Science: as of FS 2025 will count as elective course, but not anymore as a core course.)
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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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