Operator learning for models of tear film breakup
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.
Fields of Study & Subjects
Model Reduction and Neural Networks
Ocular Surface and Contact Lens
Fluid Dynamics and Thin Films
Publication details
- Journal / source
- Mathematical Medicine and Biology A Journal of the IMA
- Volume
- 43
- Issue
- 3
- Pages
- 177–201
- Publication date
- 20 August 2026
- Language
- EN