arXiv:2601.08001math.NAcs.CV2026-01
用神经算子替代传统反问题求解，快速分析泪膜破裂动态
Operator learning for models of tear film breakup
- 用神经算子学习泪膜动态，替代复杂反问题求解
- 训练数据来自模拟的泪膜演化，实现高效建模
- 适合干眼病研究与实时泪膜分析场景
泪膜（TF）破裂是理解干眼病的关键，但通过荧光（FL）成像估算泪膜厚度和渗透压通常需要求解计算量大的反问题。本文提出一种算子学习框架，用在模拟泪膜动力学数据上训练的神经算子取代传统反问题求解器。该方法为泪膜动力学的快速、数据驱动分析提供了可扩展路径。
原文摘要 · Abstract (English)
Tear film (TF) breakup is a key driver of understanding dry eye disease, yet estimating TF thickness and osmolarity from fluorescence (FL) imaging typically requires solving computationally expensive inverse problems. We propose an operator learning framework that replaces traditional inverse solvers with neural operators trained on simulated TF dynamics. This approach offers a scalable path toward rapid, data-driven analysis of tear film dynamics.
算子学习泪膜分析干眼病
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