Title | ||
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A Fluid Flow Data Set For Machine Learning And Its Application To Neural Flow Map Interpolation |
Abstract | ||
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In recent years, deep learning has opened countless research opportunities across many different disciplines. At present, visualization is mainly applied to explore and explain neural networks. Its counterpart-the application of deep learning to visualization problems-requires us to share data more openly in order to enable more scientists to engage in data-driven research. In this paper, we construct a large fluid flow data set and apply it to a deep learning problem in scientific visualization. Parameterized by the Reynolds number, the data set contains a wide spectrum of laminar and turbulent fluid flow regimes. The full data set was simulated on a high-performance compute cluster and contains 8000 time-dependent 2D vector fields, accumulating to more than 16 TB in size. Using our public fluid data set, we trained deep convolutional neural networks in order to set a benchmark for an improved post-hoc Lagrangian fluid flow analysis. In in-situ settings, flow maps are exported and interpolated in order to assess the transport characteristics of time-dependent fluids. Using deep learning, we improve the accuracy of flow map interpolations, allowing a more precise flow analysis at a reduced memory IO footprint. |
Year | DOI | Venue |
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2021 | 10.1109/TVCG.2020.3028947 | IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS |
Keywords | DocType | Volume |
Machine learning, Interpolation, Image resolution, Data visualization, Convolutional neural networks, Feature extraction, Scientific visualization, deep learning, flow maps | Journal | 27 |
Issue | ISSN | Citations |
2 | 1077-2626 | 2 |
PageRank | References | Authors |
0.37 | 28 | 3 |
Name | Order | Citations | PageRank |
---|---|---|---|
Jakob Jakob | 1 | 2 | 0.37 |
Markus H. Gross | 2 | 10154 | 549.95 |
Tobias Günther | 3 | 35 | 8.34 |