Zobrazeno 1 - 10
of 44
pro vyhledávání: '"Um, Kiwon"'
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 space for a neural network. In contrast to previous work, this paper tr
Externí odkaz:
http://arxiv.org/abs/2211.11298
This digital book contains a practical and comprehensive introduction of everything related to deep learning in the context of physical simulations. As much as possible, all topics come with hands-on code examples in the form of Jupyter notebooks to
Externí odkaz:
http://arxiv.org/abs/2109.05237
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 for a robust and reliable evaluation is to use a reference video from a
Externí odkaz:
http://arxiv.org/abs/2011.10257
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 reconstructions from a small number of video streams. Central components
Externí odkaz:
http://arxiv.org/abs/2011.10284
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 learning methods can improve the solution accuracy by correcting for effects
Externí odkaz:
http://arxiv.org/abs/2007.00016
Publikováno v:
Proceedings of Machine Learning Research 119 (2020) 5349-5360
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 time-dependent 2D data that commonly arises from motion and transport-
Externí odkaz:
http://arxiv.org/abs/2002.07863
Comparative evaluation lies at the heart of science, and determining the accuracy of a computational method is crucial for evaluating its potential as well as for guiding future efforts. However, metrics that are typically used have inherent shortcom
Externí odkaz:
http://arxiv.org/abs/1907.04179
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 the fluid-implicit-particle method using training data acquired from phy
Externí odkaz:
http://arxiv.org/abs/1704.04456
Akademický článek
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Autor:
Kee, Min Hyung1 (AUTHOR), Um, Kiwon2 (AUTHOR), Kang, HyunMo1 (AUTHOR), Han, JungHyun1 (AUTHOR)
Publikováno v:
Computer Graphics Forum. May2023, Vol. 42 Issue 2, p225-233. 9p. 7 Color Photographs, 1 Black and White Photograph, 2 Diagrams, 1 Chart.
