Operator learning for models of tear film breakup.

Authors

Qinying Chen, Arnab Roy, Tobin A Driscoll

Published in

Mathematical medicine and biology : a journal of the IMA. Aug 21, 2026. Epub Aug 21, 2026.

Abstract

Tear film (TF) breakup is a key driver of understanding dry eye disease, and 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.

PMID:
42625295
Bibliographic data and abstract were imported from PubMed on 21 Aug 2026.

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