{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T12:04:34Z","timestamp":1773230674593,"version":"3.50.1"},"reference-count":27,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T00:00:00Z","timestamp":1773100800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T00:00:00Z","timestamp":1773100800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Microscopy"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Models of the fluid\u2013structure interaction (FSI) model for the air\u2010puff test were analysed. Using Abaqus, the air\u2010puff test is applied to eyes with varying biomechanical parameters, such as material properties, corneal thickness, and radius. A reduced order model of the air puff (a turbulent impinging jet) has been acquired to decrease simulation time from 48 h for the FSI model to approximately 12 min for the finite element analysis (FEA) model alone. To further accelerate simulations and improve model accuracy, Physics\u2010Informed Neural Networks (PINNs) will be integrated with the reduced\u2010order model. This hybrid approach will help expand the model to a larger dataset, enhancing intraocular pressure (IOP) estimation accuracy and the corneal material properties algorithm through inverse FEA. Additionally, a neural network (NN) framework with embedded Gaussian\u2010modulated waveforms is proposed to model the pressure and deformation distributions on the corneal surface as functions of spatial and temporal parameters. By learning the relationship between corneal biomechanical inputs such as corneal central thickness (CCT), intraocular pressure (IOP), and baseline properties (), and the governing coefficients of pressure and deformation, the network accurately reconstructs the result that matches well with the high\u2010fidelity CFD data. This approach can quickly capture the distribution of pressure and deformation. It can also provide insights into the distinct spatial and temporal dynamics of pressure and deformation, giving a more comprehensive understanding of fluid\u2013structure interaction phenomena in the air\u2010puff\u00a0test.<\/jats:p>","DOI":"10.1111\/jmi.70077","type":"journal-article","created":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T15:25:59Z","timestamp":1773156359000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Reduced order modelling of air\u2010puff test for corneal material characterisation"],"prefix":"10.1111","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6893-2654","authenticated-orcid":false,"given":"Osama M.","family":"Maklad","sequence":"first","affiliation":[{"name":"Centre for Advanced Manufacturing and Materials University of Greenwich London UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5998-0021","authenticated-orcid":false,"given":"Muting","family":"Hao","sequence":"additional","affiliation":[{"name":"Oxford Thermofluid Institute, St John's College University of Oxford Oxford UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2026,3,10]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/nrdp.2016.67"},{"issue":"1","key":"e_1_2_8_3_1","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.jcrs.2004.10.044","article-title":"Determining in vivo biomechanical properties of the cornea with an ocular response analyzer","volume":"31","author":"Luce D. 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