20 citations · 60 across the 5 of their papers we have counts for
6 papers
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…
A Combined Data-driven and Physics-driven Method for Steady Heat Conduction Prediction using Deep Convolutional Neural Networks
Hao Ma, Xiangyu Hu, Yuxuan Zhang +2
With several advantages and as an alternative to predict physics field, machine learning methods can be classified into two distinct types: data-driven relying on training data and…
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…
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…
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…
Liquid Splash Modeling with Neural Networks
Kiwon Um, Xiangyu Hu, Nils Thuerey
This paper proposes a new data-driven approach to model detailed splashes for liquid simulations with neural networks. Our model learns to generate small-scale splash detail for th…