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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.<\/jats:p>","DOI":"10.1093\/imammb\/dqag009","type":"journal-article","created":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T11:49:11Z","timestamp":1787226551000},"page":"177-201","source":"Crossref","is-referenced-by-count":0,"title":["Operator learning for models of tear film breakup"],"prefix":"10.1093","volume":"43","author":[{"given":"Qinying","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Mathematical Sciences , University of Delaware, Newark, DE 19716, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arnab","family":"Roy","sequence":"additional","affiliation":[{"name":"Department of Mathematical Sciences , University of Delaware, Newark, DE 19716, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tobin A","family":"Driscoll","sequence":"additional","affiliation":[{"name":"Department of Mathematical Sciences , University of Delaware, Newark, DE 19716, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,8,21]]},"reference":[{"key":"2026100805220310700_ref1","doi-asserted-by":"crossref","first-page":"4234","DOI":"10.1167\/iovs.19-27802","article-title":"Changes in corneal detection thresholds after repeated tear film instability","volume":"60","author":"Awisi-Gyau","year":"2019","journal-title":"Invest. 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