VVZ API is not affiliated with ETH Zurich. Data might be outdated or incorrect. Please view the official ETHZ Vorlesungsverzeichnis for binding information.

401-4656-21L 6 Credits DR , MSC D-MATH
You're viewing possible stale or outdated data. Please check the latest semester for more up-to-date information.

AI in the Sciences and Engineering

Lecturers & Examiners: Prof. Dr. Siddhartha Mishra
Aimed at students in a Master's Programme in Mathematics, Engineering and Physics.
VVZ CR 4.2

Last Updated: 2026-02-05 16:29:40

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
DR , 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
  • Thu 08:15-10:00 (ML H 44)
2 h weekly
exercise AI in the Sciences and Engineering
schedule to be confirmed
  • Mon 12:15-14:00 (ML H 44)
2 h weekly

Offered In

  • Computational Science and Engineering Master
    • 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.)
  • Mathematics Master
    • 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.)
    • 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.)
  • 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.)
  • Doctorate Mathematics (More Information at: )
    • Subject Specialisation (The list of courses eligible for doctoral students is published each semester in the newsletter of the ZGSM.)