Fakultät Informatik

Advanced Deep Learning for Physics

Nils Thuerey, Kiwon Um, Marie-Lena Eckert

Advanced Deep Learning for Physics (IN 2298)

Time, Place:

Thursday, 10:00-12:00,  HS2

[Wednesday , 16:00-18:00  HS2]

First lecture: Thursday, April 12, 2018


moodle page


Apr. 11, 16:16 lecture

Apr. 12, no lecture

Apr. 18, no lecture

Apr. 19, 10:15 lecture

Apr. 25, no lecture

Apr. 26, 10:15 lecture

May 2, 16:15 exercise session

May 3, 10:15 lecture





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, you can find a summary here:  physics-based deep learning resarch. 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.  http://www.deeplearningbook.org
  • M. Nielsen: Neural Networks and Deep Learning, 2016. http://neuralnetworksanddeeplearning.com/
Fluid Simulation
  • R. Bridson, M. Mueller-Fischer: Fluid Simulation for Computer Graphics; http://www.cs.ubc.ca/~rbridson/fluidsimulation/fluids_notes.pdf
  • 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



- In collaboration with partners from industry, we have a number of thesis topics available in the area of point-based rendering, geo-localization using public data, scene fusion from different viewpoints. If you are interested, please contact  westermann(at)tum.de


- Game Devel. Lab Practical for SS'18: kick off meeting on April 3rd,  details here. No matching system sign-up needed!


- One PhD position on   Turbulence Visualization is available at the Computer Graphics & Visualization group.