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<p>Effective joint visualization of flow and scalar fields is crucial for advancing scientific and engineering insights. Traditional streamline seeding techniques focus on flow field characteristics, often neglecting the scalar field’s contribution to comprehensive data interpretation. We introduce a dual-guided minimum correlation point seeding algorithm that integrates scalar fields as a critical measure for modulating seeding density to address this limitation. By conceptualizing streamlines as heat sources and unrepresented regions as heat sinks, our algorithm employs the physical principles of thermal diffusion to identify optimal seeding locations. This approach dynamically generates streamlines through an iterative process, ensuring comprehensive flow and scalar field feature coverage.Our experiments demonstrate the superiority of the proposed method over conventional techniques, achieving greater expressiveness and accuracy in visualizing multi-field data. This work underscores the potential of our algorithm to advance joint visualization techniques in diverse scientific domains. Our source code is publicly available at <a href="https://github.com/sxdxdxd/dual-guided-minimum-correlation-point-seeding">https://github.com/sxdxdxd/dual-guided-minimum-correlation-point-seeding</a>.</p>

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Thermally guided streamline placement for joint flow and scalar field visualization

  • 期刊论文

  • 发表日期：

    2025年3月26日

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  • Xiangda Shen,
  • Yanci Zhang

摘要

Effective joint visualization of flow and scalar fields is crucial for advancing scientific and engineering insights. Traditional streamline seeding techniques focus on flow field characteristics, often neglecting the scalar field’s contribution to comprehensive data interpretation. We introduce a dual-guided minimum correlation point seeding algorithm that integrates scalar fields as a critical measure for modulating seeding density to address this limitation. By conceptualizing streamlines as heat sources and unrepresented regions as heat sinks, our algorithm employs the physical principles of thermal diffusion to identify optimal seeding locations. This approach dynamically generates streamlines through an iterative process, ensuring comprehensive flow and scalar field feature coverage.Our experiments demonstrate the superiority of the proposed method over conventional techniques, achieving greater expressiveness and accuracy in visualizing multi-field data. This work underscores the potential of our algorithm to advance joint visualization techniques in diverse scientific domains. Our source code is publicly available at https://github.com/sxdxdxd/dual-guided-minimum-correlation-point-seeding.