198 citations · 374 across the 5 of their papers we have counts for
5 papers
Learning Distributions of Complex Fluid Simulations with Diffusion Graph Networks
Mario Lino, Tobias Pfaff, Nils Thuerey
Physical systems with complex unsteady dynamics, such as fluid flows, are often poorly represented by a single mean solution. For many practical applications, it is crucial to acce…
Physics-based Deep Learning
N. Thuerey, B. Holzschuh, P. Holl +5
This document is a hands-on, comprehensive guide to deep learning in the realm of physical simulations. Rather than just theory, we emphasize practical application: every concept i…
Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers
Kiwon Um, Robert Brand, Yun +3
Finding accurate solutions to partial differential equations (PDEs) is a crucial task in all scientific and engineering disciplines. It has recently been shown that machine learnin…
Learning to Control PDEs with Differentiable Physics
Philipp Holl, Vladlen Koltun, Nils Thuerey
Predicting outcomes and planning interactions with the physical world are long-standing goals for machine learning. A variety of such tasks involves continuous physical systems, wh…
Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
Mengyu Chu, You Xie, Jonas Mayer +2
Our work explores temporal self-supervision for GAN-based video generation tasks. While adversarial training successfully yields generative models for a variety of areas, temporal…