Jakob Jakob, Gross Markus, Gunther Tobias
IEEE Trans Vis Comput Graph. 2021 Feb;27(2):1279-1289. doi: 10.1109/TVCG.2020.3028947. Epub 2021 Jan 28.
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.
近年来，深度学习在许多不同学科中开启了无数的研究机遇。目前，可视化主要用于探索和解释神经网络。而其对应的领域——将深度学习应用于可视化问题——要求我们更开放地共享数据，以便让更多科学家能够参与数据驱动的研究。在本文中，我们构建了一个大型流体流动数据集，并将其应用于科学可视化中的一个深度学习问题。该数据集以雷诺数为参数，包含了广泛的层流和湍流流体流动状态。完整的数据集在高性能计算集群上进行了模拟，包含8000个随时间变化的二维矢量场，数据量累计超过16TB。利用我们的公共流体数据集，我们训练了深度卷积神经网络，以便为改进的事后拉格朗日流体流动分析设定一个基准。在原位设置中，流图被导出并进行插值，以评估随时间变化的流体的输运特性。通过深度学习，我们提高了流图插值的准确性，从而在减少内存I/O占用的情况下实现更精确的流动分析。
