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    This study introduces the Merge Tree Neural Network (MTNN) for fast and accurate merge tree comparisons. This AI approach significantly speeds up analysis, making complex data visualization more efficient.

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    Area of Science:

    • Scientific Visualization
    • Topological Data Analysis
    • Machine Learning

    Background:

    • Merge trees are crucial for scalar field visualization but comparisons are computationally intensive.
    • Existing methods rely on exhaustive node matching, limiting efficiency.

    Purpose of the Study:

    • To develop a computationally efficient and accurate method for merge tree comparison.
    • To introduce the Merge Tree Neural Network (MTNN) for rapid similarity computation.

    Main Methods:

    • Utilized graph neural networks to generate vector embeddings of merge trees.
    • Developed the MTNN model incorporating topological attention for enhanced similarity.
    • Trained and validated the model on real-world datasets across various domains.

    Main Results:

    • The MTNN achieves high-quality similarity computation for merge trees.
    • Demonstrated significant speedup (over 100×) compared to prior state-of-the-art methods.
    • Maintained a low error rate (<0.1%) on benchmark datasets.

    Conclusions:

    • The MTNN offers a superior approach for merge tree comparison in terms of accuracy and efficiency.
    • The model shows generalizability across diverse datasets.
    • This advancement facilitates more effective scientific visualization and data analysis.