My current research topic is the similarity assessment of data from numerical PDE simulations. In short, common comparison operations for this data like L¹ or L² metrics are suboptimal, as the only consider local distances without accounting for the context or larger structures. I’m investigating deep learning methods to create more accurate and robust comparison models for this task. In addition, I’m interested in more general topics in physics-based simulations, machine learning, computer graphics, and game development.

I’m a Ph.D. student in Nils Thuerey’s group since November 2019. I received my M.Sc. and B.Sc degrees in Informatics: Games Engineering at the Technical University of Munich as well.


E-mail: georg.kohl (at)
Phone: +
Room: 02.13.041




Supervised Theses

  • Björn Kremser, “Learned PDE Corrections for Fluid Flows with Diffusion Models”, B.Sc. Thesis, TUM, December 2023
  • Hanfeng Wu, “Perceptual Losses for Deep Learning on Fluid Simulations”, B.Sc. Thesis, TUM, September 2021
  • Benjamin Holzschuh, “Transfer Learning for Physical Simulations”, M.Sc. Thesis, TUM, March 2021