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

Prof. Dr. Nils Thuerey , Benjamin Holzschuh, Qiang Liu


Every Wednesday, 14:00-16:00 


in room: MI 02.13.010


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.

Introduction - Slides


No Date Presenter Paper Advisor
1 8.11.2023 Felix M.  Lagrangian Fluid Simulation with Continuous Convolutions Benjamin
2 8.11.2023 Omar A.  Neural Ordinary Differential Equations Qiang
3 15.11.2023 Ece S. Learning to control PDEs with differentiable physics Benjamin
4 15.11.2023 Chih-Hsiao Y. Learning data-driven discretizations for partial differential equations Qiang
5 22.11.2023 Anis Y.  Combining Differentiable PDE Solvers and Graph Neural Networks for Fluid Flow Prediction Benjamin
6 22.11.2023 Benjamin S. Message Passing Neural PDE Solvers Qiang
7 29.11.2023 Julian S.  Neural Solvers for Fast and Accurate Numerical Optimal Control Benjamin
8 29.11.2023 Tarik I.  Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers Qiang
9 6.12.2023 Hlib K. Hamiltonian Neural Networks Benjamin
10 6.12.2023 Anil. K Physics-informed Convolutional Neural Networks for Temperature Field Prediction of Heat Source Layout without Labeled Data Qiang
11 13.12.2023 Philipp J. SINDy-PI: a robust algorithm for parallel implicit sparse identification of nonlinear dynamics Benjamin
12 13.12.2023 Cem K.  Learning Physics Constrained Dynamics Using Autoencoders Qiang
13 10.1.2024 Piotr N. Score-Based Generative Modeling through Stochastic Differential Equations Benjamin
14 10.1.2024 Nils I.  Gold-standard solutions to the Schrödinger equation using deep learning: How much physics do we need? Qiang
15 17.1.2024 Julian J. The frontier of simulation-based inference Benjamin
16 17.1.2024 Marc A. Learned Turbulence Modelling with Differentiable Fluid Solvers: Physics-based Loss-functions and Optimisation Horizons Qiang
17     Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations  
18     PFGM++: Unlocking the Potential of Physics-Inspired Generative Models  
19     Fourier Neural Operator for Parametric Partial Differential Equations  
20     A Physics-informed Diffusion Model for High-fidelity Flow Field Reconstruction  

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



  • Send your final report within two weeks after your talk (Tuesday by 23:59).
  • 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.


  • Send semi-final slides at least one week before your presentation, otherwise the presentation will be cancelled. Please also make an appointment with your advisor when you send your slides. There is an mandatory discussion between you and your advisor before your presentation. Your advisor will give your feedback on your slides.
  • Send final slides within two weeks after your presentation to us (Tuesday by 23:59).
  • Any slide style you like, prepare slides as PDF file.
  • Ensure readability (colors, images and font size).
  • Avoid using too much text.
  • Highly encouraged to do some paper-related experiments and show some results in the presentation.


  • Present your topic in English.
  • You have 25-30 minutes for presentation and 5-10 minutes for questions and discussion.
  • Please actively participate in the discussion for other presentations.
  • Please test your setup (laptop/connection to projector) before giving your presentation!


  • Missing one session is allowed, if you let us know in advance and write a short summary of the papers (ca. 1 page) in your own words.
  • Missing another session means failing the seminar (special rules for severe issues as appropriate).


Contact any time you have questions related to the seminar or your paper!