|
I'm a PhD Candidate at Technical University of Munich since April 2021 where I am supervised by Professor Nikolaus Adams. My research combines Machine Learning and Particle-Based Fluid Dynamics. More about me can be found in my Resume. If you have some questions, just drop me an email or connect with me on one of the following platforms. Email / X / Google Scholar / Github / Linkedin |
|
|
I'm interested in large-scale physics-based machine learning, geometric deep learning, implicit neural representations, turbulence modelling, and uncertainty quantification. The current goal of my research is to speed-up conventional smoothed particle hydrodynamics simulations with machine learning. |
|
Neural SPH: Improved Neural Modeling of Lagrangian Fluid Dynamics
JAX-SPH: A Differentiable Smoothed Particle Hydrodynamics Framework
LagrangeBench: A Lagrangian Fluid Mechanics Benchmarking Suite
Accelerating Molecular Graph Neural Networks via Knowledge Distillation
Learning Lagrangian Fluid Mechanics with E(3)-Equivariant Graph Neural Networks
E(3) Equivariant Graph Neural Networks for Particle-Based Fluid Mechanics
On the Relationships between Graph Neural Networks for the Simulation of Physical Systems and Classical Numerical Methods |
|
I am happy to be a teaching assistant for the following courses at TUM.
|
|
Last update 29.10.2024 |
Template by Jon Barron. |