no code implementations • 6 Sep 2022 • Lukas Prantl, Jan Bender, Tassilo Kugelstadt, Nils Thuerey
As an alternative, we present a new method based on a wavelet loss formulation, which remains transparent in terms of what is optimized.
no code implementations • NeurIPS 2021 • Han Shao, Tassilo Kugelstadt, Torsten Hädrich, Wojtek Palubicki, Jan Bender, Soeren Pirk, Dominik Michels
In this contribution, we introduce a novel method to accelerate iterative solvers for rod dynamics with graph networks (GNs) by predicting the initial guesses to reduce the number of iterations.
no code implementations • 1 Jan 2021 • Lukas Prantl, Tassilo Kugelstadt, Jan Bender, Nils Thuerey
We present a new method for reconstructing and refining complex surfaces based on physical simulations.
no code implementations • 6 Jun 2020 • Han Shao, Tassilo Kugelstadt, Torsten Hädrich, Wojciech Pałubicki, Jan Bender, Sören Pirk, Dominik L. Michels
In this contribution, we introduce a novel method to accelerate iterative solvers for physical systems with graph networks (GNs) by predicting the initial guesses to reduce the number of iterations.