Master-Seminar - Deep Learning in Physics (IN2107, IN0014)

LecturerMarie Lena Eckert, Nils Thuerey
StudiesMaster Informatics
Time, Place

Seminarraum MI  02.13.010

We., Nov. 14., 2018 12:00-16:00
We., Nov. 21., 2018 12:00-16:00
We., Nov. 28., 2018 12:00-16:00
[We., Dec. 05., 2018 12:00-16:00] 

Begin15 October 2018


Using deep learning methods for physical problems is a very quickly developing area of research. The research group of Prof. Thuerey has studied learning-based methods for Navier-Stokes problems and fluid flow applications in recent years, examples of which include learning latent-spaces for physical predictions, generative adversarial networks with temporal coherence, and the inference of Reynolds-averaged Navier-Stokes flows around airfoils. Beyond these physics-based deep learning works of the Thuerey group, this seminar will give an overview of recent developments in the field.

In this course, students will autonomously investigate recent research about machine learning techniques in the physical simulation area. Independent investigation for further reading, critical analysis, and evaluation of the topic are required.


Participants are required to first read the assigned paper and start writing a report. This will help you prepare for your presentation.
  • It is only allowed to miss a single time-slot. Missing a second one means failing the seminar. If you have to miss any, please let us know in advance.
  • An advisor is assigned to each one with the paper.
  • Two weeks before the talk there will be a mandatory meeting with your advisor to review the report and discuss the structure of the presentation.
  • A short report (4 pages max., excluding references in the ACM SIGGRAPH TOG format (acmtog) - you can download the precompiled latex template) should be prepared before the meeting with the advisor.
  • Guideline: You can begin with writing a summary of the work you present as a start point; but, it would be better if you focus more on your own research rather than just finishing with the summary of the paper. We, including you, are not interested in revisiting the work done before; it is more meaningful if you make an effort to put your own reasoning about the work, such as pros and cons, limitation, possible future work, your own ideas for the issues, etc.
Presentation (slides)
  • The participants have to present their topics in a talk (in English), which should last 30 minutes. Don't put too many technical details into the talk, make sure the audience gets the paper's main idea. Be prepared to answer questions regarding the technical details, you could prepare backup slides for that.
  • Afterwards, a short discussion session will follow.
  • Plagiarism is important; please do not simply copy the original authors' slides. You can certainly refer to them.
  • The semi-final slides (PDF) should be sent one week before the talk; otherwise, the talk will be canceled. We strongly encourage you to finalize the semi-final version as far as possible. We will take a look at the version and give feedback. You can revise your slides until your presentation.
  • Be ready in advance. We suggest testing the machines you are going to use before the lecture starts. You can bring your laptop or ask us one (also any converter you need for the projector) in advance. A laser pointer will be provided, so you can use if you want.
  • The final slides and report should be sent after the talk.


Date Presenter Paper
Nov. 14.Stephan Pirner2016, Tompson et al., Accelerating Eulerian Fluid Simulation With Convolutional Networks,
Nov. 21.David Wagner2016, Li et al., To Fall Or Not To Fall: A Visual Approach to Physical Stability Prediction,
Nov. 21.Moritz Kohr2017, Watters et al., Visual Interaction Networks,
Nov. 14.Abraham Duplaa2018, Schenck, SPNets: Differentiable Fluid Dynamics for Deep Neural Networks,
Nov. 28.Bakar Andguladze2018, Luo et al., DeepWarp: DNN-based Nonlinear Deformation,
Nov. 14.Georg Kohl2015, Ladicky et al., Data-driven Fluid Simulations using Regression Forests, ACM Trans. Graph.
Nov. 21.Konstantin Kraus2018, Kim et al., Deep Fluids: A Generative Network for Parameterized Fluid Simulations,
Nov. 28.Abdelkader Saad2016, Holden et al., A Deep Learning Framework for Character Motion Synthesis and Editing, ACM Trans. Graph.
Nov. 21.Nicolas Pinkau2018, Erhardt et al., Unsupervised Intuitive Physics from Visual Observations,
Nov. 28.Maximilian Schmidt2018, Baque et al., Geodesic Convolutional Shape Optimization,
--2018, Pathak et al., Model-Free Prediction of Large Spatiotemporally Chaotic Systems from Data: A Reservoir Computing Approach, Physical review letters
Nov. 14.Akshay Tumkur Renukaprasad2018, Beck et al., Deep Neural Networks for Data-Driven Turbulence Models,
Nov. 28.Jan Fahlbusch2018, Bailey et al., Fast and deep deformation approximations, ACM Trans. Graph.
Nov. 14.Yang Liu2018, Y. Xie et al., tempoGAN: A Temporally Coherent, Volumetric GAN for Super-resolution Fluid Flow, ACM Trans. Graph.
Nov. 21.Daniil Zauzolkov2018, Wiewel et al., Latent-space Physics: Towards Learning the Temporal Evolution of Fluid Flow,
--2016, C. Yang et al., Data-driven projection method in fluid simulation, Computer Animation and Virtual Worlds

You can access the papers through TUM library's eAccess.