My current research focuses on deep learning in the area of fluid simulations. The basic idea is to accelerate and refine the simulations with machine learning techniques. Accordingly, part of my research deals with the intelligent interpolation of fluid simulations. In addition, I am currently also investigating upsampling methods for particle-based data.
Since 2018 I’m a Ph.D. candidate in Nils Thuerey’s group.
E-mail: lukas.prantl (at) tum.de
- Lukas Prantl, Boris Bonev, and Nils Thuerey, “Generating Liquid Simulations with Deformation-aware Neural Networks“, ICLR 2019, last update March 2019 [Project]
- Nils Thuerey, Konstantin Weissenow, Lukas Prantl and Xiangyu Hu, “Deep Learning Methods for Reynolds-Averaged Navier-Stokes Simulations of Airfoil Flows“, AIAA (2019) [Project]
- Lukas Prantl, Nuttapong Chentanez, Stefan Jeschke, and Nils Thuerey, “Tranquil Clouds: Neural Networks for Learning Temporally Coherent Features in Point Clouds“, ICLR 2020, last update January 2020 [Project]
- Benjamin Ummenhofer, Lukas Prantl, Nils Thuerey, and Vladlen Koltun, “Lagrangian Fluid Simulation with Continuous Convolutions“, ICLR 2020, last update December 2019
- Lukas Prantl, “Neural Networks for Particle-based Fluid Data”, M.Sc. Thesis, Technische Universität München, April 2018. (English)
- Lukas Prantl, “Interactive Interpolation of Fluid Simulations”, B.Sc. Thesis, Technische Universität München, August 2015. (English)
Talks and Posters
- “Generating Liquid Simulations with Deformation-aware Neural Networks”, poster presentation, ICLR, May 2019
- “Tranquil Clouds: Neural Networks for Learning Temporally Coherent Features in Point Clouds”, spotlight and poster presentation, ICLR, April 2020
- Winter 2019/20: Seminar Deep Learning in Physics
- Summer 2019: Grundlagen Algorithmen und Datenstrukturen
- Winter 2018/19: Seminar Deep Learning in Physics
- “Adversarial training of airfoil flow-predictions” – Daniel Matter, Bachelor’s Thesis, 2019