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Automatic Semantic Alignment of Flow Pattern Representations for Exploration with Large Language Models.

Weihan Zhang, Jun Tao

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    |November 20, 2025
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    Summary
    This summary is machine-generated.

    This study introduces an automated framework connecting flow patterns to large language models (LLMs) for intuitive scientific visualization. It enables natural language querying of complex flow data, enhancing accessibility for domain experts.

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    Area of Science:

    • Fluid dynamics
    • Scientific visualization
    • Artificial intelligence

    Background:

    • Traditional flow visualization interfaces require specialized knowledge and interaction techniques.
    • Natural language interaction offers intuitive control but faces challenges in scientific concept recognition and data extraction.
    • Manual labeling of flow patterns for machine learning is labor-intensive and limits scalability.

    Purpose of the Study:

    • To develop an automated framework for aligning flow pattern representations with large language model (LLM) semantic spaces.
    • To enable intuitive querying and visualization of complex flow structures using natural language.
    • To eliminate the need for manual labeling in flow pattern analysis.

    Main Methods:

    • Encoding streamline segments using a denoising autoencoder.
    • Mapping flow pattern representations to LLM embeddings via a projector layer.
    • Utilizing an attention mechanism for semantic matching between text and flow data.

    Main Results:

    • Successfully aligned flow pattern representations with LLM semantic space without manual labeling.
    • Enabled extraction of flow patterns based on natural language descriptions.
    • Demonstrated effectiveness through case studies in intuitive and intelligent flow exploration.

    Conclusions:

    • The proposed framework provides an accessible and intelligent approach to exploring complex flow data.
    • Natural language interaction significantly enhances user experience in scientific visualization.
    • Automated alignment with LLMs offers a scalable solution for analyzing diverse scientific concepts in flow patterns.