Latent Space Subdivision: Stable and Controllable Time Predictions for Fluid Flow

We propose an end‐to‐end trained neural network architecture to robustly predict the complex dynamics of fluid flows with high temporal stability. We focus on single‐phase smoke simulations in 2D and 3D based on the incompressible Navier‐Stokes (NS) equations, which are relevant for a wide range of...

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Vydané v:	Computer graphics forum Ročník 39; číslo 8; s. 15 - 25
Hlavní autori:	Wiewel, S., Kim, B., Azevedo, V. C., Solenthaler, B., Thuerey, N.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Oxford Blackwell Publishing Ltd 01.12.2020
Predmet:	Artificial neural networks CCS Concepts Compression tests Computer architecture Computing methodologies → Neural networks Designers Dynamic stability Energy management Fluid dynamics Fluid flow Incompressible flow Neural networks Photovoltaic cells Physical simulation
ISSN:	0167-7055, 1467-8659
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