A Fluid Flow Data Set for Machine Learning and its Application to Neural Flow Map Interpolation

Jakob J, Gross M, Günther T (2021)

Publication Language: English

Publication Type: Journal article, Original article

Publication year: 2021

Journal

IEEE Transactions on Visualization and Computer Graphics Institute of Electrical and Electronics Engineers (IEEE)

DOI: 10.1109/TVCG.2020.3028947

Abstract

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.

Authors with CRIS profile

Tobias Günther

Involved external institutions

Eidgenössische Technische Hochschule Zürich (ETHZ) / Swiss Federal Institute of Technology in Zurich

Switzerland (CH)

How to cite

APA:

Jakob, J., Gross, M., & Günther, T. (2021). A Fluid Flow Data Set for Machine Learning and its Application to Neural Flow Map Interpolation. IEEE Transactions on Visualization and Computer Graphics. https://dx.doi.org/10.1109/TVCG.2020.3028947

MLA:

Jakob, Jakob, Markus Gross, and Tobias Günther. "A Fluid Flow Data Set for Machine Learning and its Application to Neural Flow Map Interpolation." IEEE Transactions on Visualization and Computer Graphics (2021).

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