Operator learning for models of tear film breakup

Abstract: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.

Numerical Analysis (math.NA); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)

arXiv:2601.08001 [math.NA]

(or arXiv:2601.08001v2 [math.NA] for this version)

https://doi.org/10.48550/arXiv.2601.08001 Focus to learn more arXiv-issued DOI via DataCite

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