Physics-based Deep Learning (Thuerey Group)

Deep learning algorithms for physical problems are a very active field of research. The group of Prof. Thuerey is very actively pursuing this area, which could be summarized as "physics-based deep learning". A particular focus lies on artificial neural networks for Navier-Stokes problems. You can find a summary here: physics-based deep learning research


mantaflow & tensorflow

Many of our research projects are based on a common codebase, the mantaflow solver. This solver is an open-source framework targeted at fluid simulation research in Computer Graphics. It has a parallelized C++ solver core, a high-level python API for defining scenes and quickly adapting the solvers. It is tailored towards quickly prototyping and testing new algorithms. Recently, we’ve also added tools and plugins to interface with the tensorflow deep learning framework. The long term goal is to build a flexible platform for machine learning projects involving convolutional neural networks and fluid flow. Below, you can find an introduction to get started with manta & tensor-flow, and more detailed tutorials will follow soon.

ERC Starting Grant realFlow

This research is supported by Nils Thuerey's ERC Starting Grant “realFlow – Virtualization of Real Flows for Animation and Simulation” (StG-2015-637014).