The focus of our research is to develop numerical methods for physics simulations in computer graphics. A particular emphasis lies on simulating fluid flow with deep learning techniques, especially with convolutional neural networks. These methods have significant potential for efficient and robust simulations in the computer graphics and digital games context. Beyond this, we also target applications from the fields of mechanical engineering and medicine.
- CNN-patches: We compute flow descriptors based on flow invariatns, which we use these to look up pre-computed patches of 4D data.
- ML-FLIP: This data-driven model captures sub-grid scale formation of droplets for liquid simulations.
- tempoGAN: Our GAN approach directly synthesizes a temporally coherent state of an advected quantity, such as smoke.
- Latent-space physics: This work focuses on pressure fields over time. In contrast to the others, it predicts the temporal evolution using the latent-space of a trained encoder network.
- Neural Liquid Drop: this method captures full solutions for classes of liquid problems in terms of space-time deformations, allowing for real-time interactions.
mantaflow: 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.
Just in case you haven’t found it yet, the official mantaflow homepage is this one: http://mantaflow.com
realFlow: A significant part of our group is funded by the ERC Starting Grant realFlow. This grant, with a total volume of almost 1.5 million euro, is aimed at novel simulation and reconstruction algorithms for fluid flows. The full title is “realFlow – Virtualization of Real Flows for Animation and Simulation” (StG-2015-637014). It’s goal is to improve the simulation of physical processes and, above all, make it possible to generate such simulations more quickly and realistically. A central component of this research are data-driven methods, and especially machine learning techniques with deep neural networks.