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

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.

Requirements

• 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.
• 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.
• A short report (approximately 3-4 pages in the ACM SIGGRAPH TOG format (acmtog)) should be prepared and sent within two weeks after the talk. When you send the report, please send the final slides (PDF) as well.
• 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.

References

• 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