My current research focuses on deep learning in the area of liquid simulations. The basic idea is to accelerate and refine the simulations with machine learning techniques. Accordingly, part of my research deals with learning reduced representations of fluid simulations or investigating upsampling methods for particle-based data. In recent work, I have also tried to increase the robustness of learning fluid behavior using physically induced biases.

Since 2018 I’m a Ph.D. candidate in Nils Thuerey’s group.


E-mail: lukas.prantl (at)
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Room: 02.13.060



  • Lukas Prantl, “Deep Learning Methods for Simulation of Liquids”, Doctoral Thesis, Technical University of Munich, September 2022. (English)
  • Lukas Prantl, “Neural Networks for Particle-based Fluid Data”, M.Sc. Thesis, Technical University of Munich, April 2018. (English)
  • Lukas Prantl, “Interactive Interpolation of Fluid Simulations”, B.Sc. Thesis, Technical University of Munich, 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
  • “Guaranteed Conservation of Momentum for Learning Particle-based Fluid Dynamics”, oral and poster presentation, NeurIPS, December 2022


Supervised Theses

  • Adversarial training of airfoil flow-predictions” – Daniel Matter, Bachelor’s Thesis, 2019