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

Lecturer Lukas Prantl, Nilam Tathawadekar, Stephan Rasp
Studies Master Informatics
Time Mondays, 12:00-14:00

Seminarraum MI 02.13.010

Kick-Off: Monday, January 27., 2020  from 16:00-18:00  in 02.13.010

Begin Monday, April 20., 2020


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.

Virtual Seminar

  • For our seminar we use BigBlueButton:
  • Please remain muted unless you have permission to speak. 
  • If you have a question or comment, please let us know in the chat, we will let you know as soon as you can speak, or post it directly in the chat.
Hardware Setup

A Laptop with built in microphone and speakers is terrible for everyone else in the virtual meeting!

  • Laptop fan makes noise (fan will most probably rev up after a couple of minutes, especially if you share your screen and if there are many users in the virtual meeting).
  • Typing and/or touchpad sounds will be transferred to communication partners.
  • Most likely there will be echo/feedback from the speakers.
  • Use the headset that came with your mobile phone. Even the cheapest headset will perform better than the built in microphone in a notebook.
  • (Cheap) bluetooth headsets are okay as well, but they perform not as good as wired ones!
  • Use an external microphone in combination with headphones. Some webcams have good quality microphones built in (e.g. Logitech).
  • Tablets like Apple iPads have quite solid built in microphones (and no fan). The same is possibly true for other tablets and even for smart phones. If you do not have a better alternative, they will most probably perform better than a Laptop.
Giving a Talk Online

Giving a talk in a virtual meeting scenario is quite different from giving a talk in a classroom scenario. You do not get any feedback from the audience during the talk (do they look happy/bored/satisfied?) and you cannot interact as easily in a non-verbal way with the audience as in the classroom, e.g. by pointing to things displayed on the projector screen. For this reason, mentally prepare yourself to talking to your screen for 20-30 minutes!

Normally, your slides should not contain too much written text. In the case of virtual meetings, some more information than usual is not wrong. Added info can help the audience to bridge gaps in case there was a glitch in the network connection, etc. Lastly, it is a good idea to open the content of the slides step by step to avoid that the audience can read faster than you talk.

Normally, when you give a talk, you are standing. This creates some tension in you, you will sound more energetic compared to talking while sitting, and, lastly, it helps you to concentrate. So it is a good idea to create a setup for giving the talk where you can stand in front of your screen.

You are supposed to give a talk. So do not write a script that you read to the audience. Reading out a pre-made script would be boring in a classroom scenario and it will be as boring in the virtual meeting!

Also important: switch off any type of messengers, notifications, etc. which can distract you. As you are pretty much isolated from the audience, it is a good idea to set a timer to keep track of elapsed time. Lastly, prepare for technical problems. Have some kind of second channel open to your advisor that can be used to contact you in case something unexpected happens.

Especially if you are using a quite “wide” (omnidirectional) microphone that sits on your desk, please avoid loud sounds from typing on your keyboard when you flip through slides while talking.

Preliminary Schedule

08 Mar 2020 Deregistration due
12 Mar 2020 Send three preferred topics
19 Mar 2020 Assign topics
20 Apr 2020 Introduction lecture 
04 May 2020 First talk


No Date Presenter Paper Advisor
01 04.05 Ankur Agrawal 2018, Pathak et al., Model-Free Prediction of Large Spatiotemporally Chaotic Systems from Data: A Reservoir Computing Approach, Physical review letters Lukas
02 04.05 Aman Saxena 2019, Raissi et al., Physics-informed Neural Networks: A Deep Learning Framework for Solving Forward and Inverse Problems Involving Nonlinear Partial Differential Equations, Journal of Computational Physics Stephan
03 - - 2019, Bar-Sinai et al., Learning data-driven discretizations for partial differential equations, PNAS  
04 11.05 Julian Hohenadel 2019, Thuerey et al., Deep Learning Methods for Reynolds-Averaged Navier-Stokes Simulations of Airfoil Flows, AIAA Lukas
05 - - 2019, Fukami et al., Super-resolution reconstruction of turbulent flows with machine learning. Journal of Fluid Mechanics, Journal of Fluid Mechanics  
06 11.05 Ashish Kumar 2019, Murata et al., Nonlinear mode decomposition with convolutional neural networks for fluid dynamics, Journal of Fluid Mechanics Nilam
07 18.05 Christopher Sendlinger 2016, Tompson et al., Accelerating Eulerian Fluid Simulation With Convolutional Networks, ICML Lukas
08 18.05 Mohamad Amoud 2016, Yang et al., Data-driven projection method in fluid simulation, Computer Animation and Virtual Worlds, Journal Computer Animation and Virtual Worlds Stephan
09 25.05 Philipp Hermüller 2018, Kim et al., Deep Fluids: A Generative Network for Parameterized Fluid Simulations, CGF Lukas
10 25.05 Thilo Müller 2015, Ladicky et al., Data-driven Fluid Simulations using Regression Forests, ACM Trans. Graph. Lukas
11 08.06 Faruk Cankaya 2020, Ummenhofer et al., Lagrangian Fluid Simulation with Continuous Convolutions, ICLR Lukas
12 - - 2018, Y. Xie et al., tempoGAN: A Temporally Coherent, Volumetric GAN for Super-resolution Fluid Flow, ACM Trans. Graph.  
13 08.06 Sultan Sinem Eren 2017, Um et al., Liquid Splash Modeling with Neural Networks, Computer Graphics Forum Stephan
14 15.06 Ashish Darekar 2018, Schenck, SPNets: Differentiable Fluid Dynamics for Deep Neural Networks, CoRL Nilam
15 - - 2019, Hu et al., ChainQueen: A Real-Time Differentiable Physical Simulator for Soft Robotics, NIPS  
16 15.06 Pushpendra Mishra 2019, Xu et al., DensePhysNet: Learning Dense Physical Object Representations via Multi-step Dynamic Interaction, RSS 2019 Nilam
17 - - 2018, Bailey et al., Fast and deep deformation approximations, ACM Trans. Graph.  
18 22.06 Lukas Hager 2016, Holden et al., A Deep Learning Framework for Character Motion Synthesis and Editing, ACM Trans. Graph. Nilam
19 22.06 Jonathan Klimesch 2020, Iten et al., Discovering physical concepts with neural networks, Physical Review Letters Stephan
19 - - 2019, Zeng et al., TossingBot: Learning to Throw Arbitrary Objects with Residual Physics, RSS  
20 - - 2018, Tan et al., Sim-to-Real: Learning Agile Locomotion For Quadruped Robots, RSS  
21 29.06 Marius Merkle 2019, Greydanus et al., Hamiltonian Neural Networks, NeurIPS Nilam
22 29.06 Samuel Knoethig 2018, Rasp et al., Deep learning to represent subgrid processes in climate models, PNAS Stephan

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