130 citations · 216 across the 7 of their papers we have counts for
8 papers
Exploring Physical Latent Spaces for High-Resolution Flow Restoration
Chloe Paliard, Nils Thuerey, Kiwon Um
We explore training deep neural network models in conjunction with physics simulations via partial differential equations (PDEs), using the simulated degrees of freedom as latent s…
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…
ScalarFlow: A Large-Scale Volumetric Data Set of Real-world Scalar Transport Flows for Computer Animation and Machine Learning
Marie-Lena Eckert, Kiwon Um, Nils Thuerey
In this paper, we present ScalarFlow, a first large-scale data set of reconstructions of real-world smoke plumes. We additionally propose a framework for accurate physics-based rec…
Perceptual Evaluation of Liquid Simulation Methods
Kiwon Um, Xiangyu Hu, Nils Thuerey
This paper proposes a novel framework to evaluate fluid simulation methods based on crowd-sourced user studies in order to robustly gather large numbers of opinions. The key idea f…
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 Similarity Metrics for Numerical Simulations
Georg Kohl, Kiwon Um, Nils Thuerey
We propose a neural network-based approach that computes a stable and generalizing metric (LSiM) to compare data from a variety of numerical simulation sources. We focus on scalar…