My PhD 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, element-wise distances without accounting for the context or larger structures. I investigated deep learning methods to create more accurate and robust comparison methods for this task. More recently, I worked with diffusion models in the context of fluid flows and created a benchmark to investigate the strengths and weaknesses of different neural network architectures for the prediction of turbulent flows. In addition, I am interested in more general topics in physics-based simulations, machine learning, computer graphics, and game development.
I was part of the physics-based simulation group between November 2019 and January 2025, and I am now working at TNG Technology Consulting.
Contact
E-mail: georg.kohl (at) tum.de
Publications
- Georg Kohl, Li-Wei Chen, and Nils Thuerey, “Benchmarking Autoregressive Conditional Diffusion Models for Turbulent Flow Simulation“, arXiv, 2023 [Project]
- Georg Kohl, Li-Wei Chen, and Nils Thuerey, “Learning Similarity Metrics for Volumetric Simulations with Multiscale CNNs“, AAAI Conference on Artificial Intelligence, 2023 [Project]
- Georg Kohl, Kiwon Um, and Nils Thuerey, “Learning Similarity Metrics for Numerical Simulations“, International Conference on Machine Learning, 2020 [Project]
Theses
- Georg Kohl, “Similarity Metrics for Numerical Simulations using Deep Learning“, Dissertation, Technical University of Munich, July 2024. (English)
- Georg Kohl, “Accuracy Evaluation of Numerical Simulation Methods with CNNs“, M.Sc. Thesis, Technical University of Munich, October 2019. (English)
- Georg Kohl, “Visual Enhancement of Liquid Simulations using Secondary Particles“, B.Sc. Thesis, Technical University of Munich, September 2017. (English) [Video]
Teaching
- Winter 2023/24: Computer Games Laboratory
- Summer 2023: Grundlagen: Algorithmen und Datenstrukturen
- Winter 2022/23: Computer Games Laboratory
- Summer 2022: Computer Games Laboratory
- Summer 2021: Grundlagen: Algorithmen und Datenstrukturen
- Winter 2020/21: Seminar – Deep Learning in Computer Graphics
- Summer 2020: Computer Games Laboratory
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