My research interests focus on the intersection of computational fluid dynamics and machine learning. In this context, my current project investigates learning-based methods for turbulence modelling with the aim of leveraging such methods to improve simulation accuracy and efficiency.
Since early 2020 I am a Ph.D. student in Nils Thuerey’s group.
Contact
E-mail: bjoern.list (at) tum.de
Room: 02.13.041
Publications
- Björn List, Mario Lino, Nils Thuerey “Rotational Equivariant Graph Neural Networks via Local Eigenbasis Transformations“, Physics of Fluids, 2025. [POF, Code]
- Björn List, Li-Wei Chen, Kartik Bali, Nils Thuerey “Differentiability in Unrolled Training of Neural Physics Simulators on Transient Dynamics“, Computer Methods in Applied Mechanics and Engineering, 2025. [CMAME, Code]
- Björn List, Li-Wei Chen, Nils Thuerey “Learned Turbulence Modelling with Differentiable Fluid Solvers: Physics-based Loss Functions and Optimisation Horizons“, Journal of Fluid Mechanics, 2022. [JFM, ArXiv, Code]
- Hao Wei, Aleksandra Franz, Björn List, Nils Thuerey “INC: An Indirect Neural Corrector for Auto-Regressive Hybrid PDE Solvers“, NeurIPS, 2025. (Accepted, to be published)
Theses
- Björn List, “Optimal Design of Dynamic Networks with Application to Multi-Agent Systems”, M.Sc. Thesis, Imperial College London, September 2019.
- Björn List, “Surface Tension Modelling in Smoothed Particle Hydrodynamics”, B.Sc. Thesis, Technische Universität München, June 2018.