Maklad, Osama ORCID: https://orcid.org/0000-0001-6893-2654, Hao, Muting and Maklad, Osama (2025) Corneal material characterisation via PINNs-based modelling of impinging jets. In: Joint event "Euromech Colloquium on Data-Driven Fluid Dynamics" & "2nd ERCOFTAC Workshop on Machine Learning for Fluid Dynamics", 02-04 Apr 2025, Mary Ward House, 5-7 Tavistock Place, London, UK.

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Abstract

Models of the fluid-structure interaction (FSI) model for the air puff test were analysed. Using Abaqus, the air puff 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 hours for the FSI model to approximately 12 minutes for
the finite element analysis (FEA) model alone [1, 2]. To further accelerate simulations and improve model accuracy, Physics-Informed Neural Networks (PINNs) will be integrated with the reduced-order 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, see Figure 1.

Item Type:	Conference or Conference Paper (Speech)
Uncontrolled Keywords:	impinging jets, PINNs, fluid-structure interaction (FSI), reduced order modelling, Corneal biomechanics
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > School of Engineering (ENG)
Related URLs:	Organisation
Last Modified:	24 Apr 2025 15:00
URI:	http://gala.gre.ac.uk/id/eprint/50231

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