Master-Seminar - Deep Learning in Physics (IN2107, IN0014)
Deep learning 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 studies, 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 physics. 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.
- 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 and sent within two weeks before the talk, i.e., by 23:59 on Monday.
- 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.
- You will present your topic in English, and the talk should last 30 minutes. After that, a discussion session for ca. 10 minutes will follow.
- The slides should be structured according to your presentation. You can use any layout or template you like.
- 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. Please make sure your presentation (including videos/demos) runs on your laptop even without Wi-Fi. We can provide you with an adapter for the projector and a clicker. If you need anything else (e.g., speakers or a laptop), please contact us in advance.
|08 Mar 2019||Deregistration due|
|12 Apr 2019||Send three preferred topics|
|19 Apr 2019||Assign topics|
|29 Apr 2019||Introduction lecture|
|27 May 2019||First talk|
You can access the papers through TUM library's eAccess.
- Thuerey group: List of Publications (including Physics-based Deep Learning works)
- Book: Bishop, Pattern Recognition and Machine Learning
- Book: Hastie et al., The Elements of Statistical Learning
- Online: Nielsen, Neural Networks and Deep Learning
- Online: Ruder, An Overview of Gradient Descent Optimization Algorithms