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

Prof. Dr. Nils Thuerey , Patrick Schnell, Björn List

Content

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.

Paper Selection

The paper list for this seminar can be found below. Please send us an email with your 5 ranked preferences by Sunday, April 7th. 

We will assign papers based on these preferences, while remaining papers will be distributed randomly. The paper-matching results will be made available before the Kick-off meeting.

Requirements

Report

When:

• A semi-final version is due one week before your talk (Monday by 23:59)
• Send your final report within two weeks after your talk (Monday by 23:59).

Style:

• Please use ACM SIGGRAPH TOG format (acmtog) precompiled latex template. You can also download the precompiled latex template.
• Maximum 4 pages excluding references.

Guidlines:

• 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.

Slides

When:

• Send semi-final slides at least one week before your presentation, together with the semi-final report. Otherwise the presentation will be cancelled. Please also make an appointment with your advisor when you send your slides. There is an mandatory discussion with your advisor in the week before your presentation. Your advisor will give your feedback on your slides. 
• Send final slides within two weeks after your presentation to us (Monday by 23:59).

Style:

• Any slide style you like, prepare slides as PDF file.

Guidelines:

• 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.

Presentation

• Present your topic in English.
• You have 25 minutes for presentation and 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!

Attendance

• 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).

Paper list

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

Preliminary Schedule

Resources

• Book: Hastie et al., The Elements of Statistical Learning
• Book/Online: Goodfellow et al., Deep Learning
• Book/Online: Dive into deep learning.
• Online: Nielsen, Neural Networks and Deep Learning
• Online: Thuerey et al. Physics-based Deep Learning
• DocTUM: How to give a great scientific talk
• Review: Current and emergingdeep-learning methods for thesimulation of fluid dynamics

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