617 citations · 646 across the 5 of their papers we have counts for
5 papers
Guaranteed Conservation of Momentum for Learning Particle-based Fluid Dynamics
Lukas Prantl, Benjamin Ummenhofer, Vladlen Koltun +1
We present a novel method for guaranteeing linear momentum in learned physics simulations. Unlike existing methods, we enforce conservation of momentum with a hard constraint, whic…
Wavelet-based Loss for High-frequency Interface Dynamics
Lukas Prantl, Jan Bender, Tassilo Kugelstadt +1
Generating highly detailed, complex data is a long-standing and frequently considered problem in the machine learning field. However, developing detail-aware generators remains an…
Tranquil Clouds: Neural Networks for Learning Temporally Coherent Features in Point Clouds
Lukas Prantl, Nuttapong Chentanez, Stefan Jeschke +1
Point clouds, as a form of Lagrangian representation, allow for powerful and flexible applications in a large number of computational disciplines. We propose a novel deep-learning…
Deep Learning Methods for Reynolds-Averaged Navier-Stokes Simulations of Airfoil Flows
Nils Thuerey, Konstantin Weissenow, Lukas Prantl +1
With this study we investigate the accuracy of deep learning models for the inference of Reynolds-Averaged Navier-Stokes solutions. We focus on a modernized U-net architecture, and…
Generating Liquid Simulations with Deformation-aware Neural Networks
Lukas Prantl, Boris Bonev, Nils Thuerey
We propose a novel approach for deformation-aware neural networks that learn the weighting and synthesis of dense volumetric deformation fields. Our method specifically targets the…