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

Prof. Dr. Nils Thuerey, Lukas Prantl, Liwei Chen

StudiesMaster Informatics
Time, Place

Seminarraum MI  02.13.010

Wednesdays, 14:00-16:00

Begin

Nov. 6., 2019

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

Attendance
  • 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.
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.
Report
  • 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 after the talk, i.e., by 23:59 on Wednesday. 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.

Papers

Date Presenter Paper
Nov. 20Sagar Garg2016, Tompson et al., Accelerating Eulerian Fluid Simulation With Convolutional Networks, arXiv.org
Dec. 11Konrad Eder2018, Schenck, SPNets: Differentiable Fluid Dynamics for Deep Neural Networks, arXiv.org
--2015, Ladicky et al., Data-driven Fluid Simulations using Regression Forests, ACM Trans. Graph.
Nov. 27

Stephen Ryan

2018, Kim et al., Deep Fluids: A Generative Network for Parameterized Fluid Simulations, arXiv.org
--

2016, Holden et al., A Deep Learning Framework for Character Motion Synthesis and Editing, ACM Trans. Graph.

Nov. 6Achraf Aroua2018, Pathak et al., Model-Free Prediction of Large Spatiotemporally Chaotic Systems from Data: A Reservoir Computing Approach, Physical review letters
Dec. 18Keerthi Gaddameedi2018, Y. Xie et al., tempoGAN: A Temporally Coherent, Volumetric GAN for Super-resolution Fluid Flow, ACM Trans. Graph.
Nov. 20Mohamed Attia2016, C. Yang et al., Data-driven projection method in fluid simulation, Computer Animation and Virtual Worlds
Nov. 27Jan Luca Watter2018, Bailey et al., Fast and deep deformation approximations, ACM Trans. Graph.
Dec. 11Amir Nourinia2019, Li et al., Learning Particle Dynamics for Manipulating Rigid Bodies, Deformable Objects, and Fluids, ICLR 2019 Conference
Nov. 13Max Oberberger2019, Thuerey et al., Deep Learning Methods for Reynolds-Averaged Navier-Stokes Simulations of Airfoil Flows, arXiv.org
Dec. 18Shucheng Yang2017, Um et al., Liquid Splash Modeling with Neural Networks, Computer Graphics Forum
Nov. 6Anna Maria Geissinger2019, 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
--2019, Zhu et al., Machine learning Methods for Turbulence Modeling in Subsonic Flows Around Airfoils, AIP
Nov. 13Roland Konlechner2018, Zhang et al., Application of Convolutional Neural Network to Predict Airfoil Lift Coefficient, arXiv.org
--2019, Raissi et al., Deep learning of vortex-induced vibrations, Journal of Fluid Mechanics
Dec. 4David Elias Drothier2019, Hu et al., ChainQueen: A Real-Time Differentiable Physical Simulator for Soft Robotics, arXiv.org
Dec. 4Anjie Guo2019, Xu et al., DensePhysNet: Learning Dense Physical Object Representations via Multi-step Dynamic Interaction, RSS 2019

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