Frequency-aware Interface Dynamics with Generative Adversarial Networks

Abstract

We present a new method for reconstructing and refining complex surfaces based on physical simulations. Taking a roughly approximated simulation as input, our method infers corresponding spatial details while taking into account how they evolve over time. We consider this problem in terms of spatial and temporal frequencies, and leverage generative adversarial networks to learn the desired spatio-temporal signal for the surface dynamics. Furthermore, we investigate the possibility to train our network in an unsupervised manner, i.e. without predefined training pairs. We highlight the capabilities of our method with a set of synthetic wave function tests and complex 3D dynamics of elasto-plastic materials.

Authors 4

1. Lukas Prantl

   Technical University of Munich

   Affiliation as printed

   TECHNICAL UNIVERSITY MUNICH

2. Tassilo Kugelstadt Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

3. Jan Bender Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

4. Nils Thuerey

   Technical University of Munich

   Affiliation as printed

   TECHNICAL UNIVERSITY MUNICH