Advanced Deep Learning for Physics (IN 2298)

Nils Thuerey, Kiwon Um, Marie-Lena Eckert

Time, Place:

Thursday, 10:00-12:00, HS2

[Wednesday , 16:00-18:00 HS2]

First lecture: Thursday, April 12, 2018


moodle page

Apr. 11 Wed, 16:15 lecture
Apr. 12 Thu, no lecture
Apr. 18 Wed, no lecture
Apr. 19 Thu, 10:15 lecture
Apr. 25 Wed, no lecture
Apr. 26 Thu, 10:15 lecture
May 2 Wed, 16:15 exercise session
May 3 Thu, 10:15 lecture
May 9 Wed, no lecture
May 10 Thu, no lecture
May 16 Wed, no lecture
May 17 Thu, 10:15 lecture
May 23 Wed, no lecture
May 24 Thu, 10:15 lecture
May 30 Wed, no lecture
May 31 Thu, no lecture
June 6 Wed, no lecture
June 7 Thu, 10:15 lecture
June 13 Wed, no lecture
June 14 Thu, 10:15 lecture
June 20 Wed, no lecture
June 21 Thu, 10:15 lecture
June 27 Wed, no lecture
June 28 Thu, 10:15 lecture
July 4 Wed, no lecture
July 5 Thu, 10:15 the last lecture
July 16 Mo, 15:15 (in 02.13.010) project presentations




Deep learning algorithms for physical problems are a very active field of research. The group of Prof. Thuerey has published a series of papers in this area, in particular regarding Navier-Stokes problems and fluids. You can find a summary here: physics-based deep learning research.

This course targets the corresponding deep learning and physics modeling foundations.

Specifically, it targets deep learning techniques and numerical simulation
algorithms for materials such as fluids and deformable objects in the context
of computer animation. The lecture and exercises will all be in English. The following topics are discussed:

  • Generative neural networks & temporal network architectures
  • Physically-based animation, fluid modeling
  • Discretizations, and partial differential equations
  • Exercises to gain hands-on experience with CNN training and fluid simulation algorithms


  • Introduction to Deep Learning (Previously called: Deep Learning for Computer Vision) 
  • Computer Gaphics Fundamentals, and Game Physics highly recommended


Machine Learning

  • Goodfellow, Bengio, Courville: Deep Learning, 2016.
  • M. Nielsen: Neural Networks and Deep Learning, 2016.

Fluid Simulation

  • R. Bridson, M. Mueller-Fischer: Fluid Simulation for Computer Graphics;
  • Griebel, Dornseifer, Neunhoeffer: Numerical Simulation in Fluid Dynamics: A Practical Introduction, Soc for Industrial & Applied Math

General Background

  • Introduction to Linear Algebra: Gilbert Strang, Wellesley-Cambridge Press
  • Computer Animation: Algorithms and Techniques, Parent, Morgan Kaufmann


If you're interested, you can also check out some of our previous work on deep learning algorithms for fluid flow. These papers demonstrate several ways of training physics-aware neural networks for different parts of Navier-Stokes solvers. 

Lecture Slides

Will be made available on the moodle page.