Operator learning for models of tear film breakup
This work addresses the challenge of analyzing tear film dynamics for understanding dry eye disease, but it is incremental as it applies an existing operator learning method to a new domain-specific problem.
The authors tackled the problem of estimating tear film thickness and osmolarity from fluorescence imaging, which traditionally requires solving computationally expensive inverse problems, by proposing an operator learning framework that replaces these solvers with neural operators trained on simulated dynamics, resulting in a scalable approach for rapid analysis.
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.