Predicting High-Resolution Turbulence Details In Space and Time
Event Type
Technical Papers
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TimeTuesday, December 1410am - 10:11am JST
LocationHall B5 (1) (5F, B Block) & Virtual Platform
DescriptionPredicting intricate details of a turbulent flow field in both space and time
from a coarse input remains a major challenge despite the availability of modern machine learning tools.
In this paper, we present a simple and effective dictionary-based approach to spatio-temporal upsampling of fluid simulation.
We demonstrate that our neural network approach can reproduce the visual complexity of turbulent flows from spatially and temporally coarse velocity fields even when using very few generic training sets.
Moreover, since our method generates finer spatial and/or temporal details through embarrassingly-parallel upsampling of small local patches, it can efficiently handle upsampling across a variety of grid resolutions. As a consequence, our method offers a whole range of applications varying from fluid flow upsampling to fluid data compression.
We test our method on a series of complex examples, highlighting dramatically better results in spatio-temporal upsampling and
flow data compression than existing methods as assessed by both qualitative and quantitative comparisons, demonstrating the efficiency and generalizability of our method for synthesizing turbulent flows.