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    Area of Science:

    • Scientific Visualization
    • Data Compression
    • Fluid Dynamics

    Background:

    • Streamline-based methods are crucial for visualizing complex flow fields.
    • Existing methods face challenges in balancing efficiency and reconstruction quality, especially in in situ settings.

    Purpose of the Study:

    • To develop a novel streamline-based approach for flow field representation and reduction.
    • To enable efficient in situ visualization and high-quality post hoc analysis of vector fields.

    Main Methods:

    • A deep learning method for vector field reconstruction using traced streamlines.
    • A two-stage process for reconstructing high-quality vector fields from compressed streamlines.
    • In situ tracing and compressed storage of streamlines for post hoc analysis.

    Main Results:

    • The proposed method achieves effective flow field representation and reduction.
    • Demonstrated qualitative and quantitative results across multiple datasets.
    • Comparison against gradient vector flow shows a favorable speed and quality tradeoff.

    Conclusions:

    • The deep learning approach offers a powerful tool for streamline-based flow field analysis.
    • The method provides a flexible solution for both in situ and post hoc visualization needs.
    • This technique advances the state-of-the-art in scientific visualization and data reduction.