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    科学领域:

    • 流体动力学 流体动力学
    • 光学诊断仪器的使用.
    • 计算物理学的计算物理.

    背景情况:

    • 莫伊雷断层扫描对于分析高温和复杂流场至关重要.
    • 传统方法耗时,资源密集,限制了诊断速度和效率.

    研究的目的:

    • 引入基于深度学习的莫雷偏移断层扫描 (DLMDT) 方法.
    • 通过使用深度学习,实现高精度预测流域中折射率分布.
    • 确保DLMDT方法与物理原理保持一致,并提高诊断速度.

    主要方法:

    • 开发一个两步深度学习模型,用于莫伊雷偏移断层扫描.
    • 通过数值模拟验证模型准确性和计算效率.
    • 应用训练DLMDT模型来诊断现实世界的流场.

    主要成果:

    • DLMDT方法准确地预测了在测量流域中的折射率分布.
    • 该模型在预测中表现出高精度和结构相似性.
    • 与传统的莫雷断层扫描技术相比,诊断速度显著提高.

    结论:

    • 拟议的DLMDT方法为高温和复杂流量场的高精度诊断提供了显著的速度改进.
    • DLMDT提供准确和物理一致的结果,与既定原则保持一致.
    • 这一进步为莫雷断层扫描应用中增强智能能力奠定了基础.