Operator learning for models of tear film breakup
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
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| _version_ | 1866912819777110016 |
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| author | Chen, Qinying Roy, Arnab Driscoll, Tobin A. |
| author_facet | Chen, Qinying Roy, Arnab Driscoll, Tobin A. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_08001 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Operator learning for models of tear film breakup Chen, Qinying Roy, Arnab Driscoll, Tobin A. Numerical Analysis Computer Vision and Pattern Recognition Machine Learning 92C35 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. |
| title | Operator learning for models of tear film breakup |
| topic | Numerical Analysis Computer Vision and Pattern Recognition Machine Learning 92C35 |
| url | https://arxiv.org/abs/2601.08001 |