20 citations · 37 across the 2 of their papers we have counts for
4 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…