Year: 2026
Type: article
Abstract: Tear film (TF) breakup is a key driver of understanding dry eye disease, and estimating TF thickness and osmolarity from fluorescence 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.
Authors: Qinying Chen, Arnab Roy, Tobin A Driscoll
Institution: University of Delaware
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7124153523