Operator learning for models of tear film breakup
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 60 references that are in the map. First posted on arXiv 12 Jan 2026.
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12 of 60 references.
References · 12
- 2018Neural Ordinary Differential Equationscited by 3,779
- 2020Fourier Neural Operator for Parametric Partial Differential Equationscited by 2,908
- 2020Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domainscited by 2,200
- 2019DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operatorscited by 2,018
- 2022Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's nextcited by 608
- 2021Physics-Informed Neural Operator for Learning Partial Differential Equationscited by 527
- 2021Neural Operator: Learning Maps Between Function Spacescited by 414
- 2020Universal Differential Equations for Scientific Machine Learningcited by 353
- 2020On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networkscited by 284
- 2022On Neural Differential Equationscited by 268
- 2022Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of nanoscale heat transportcited by 92
- 2023Operator learning with PCA-Net: upper and lower complexity boundscited by 44
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