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Parametric reduced-order modelling often serves as a surrogate method forhemodynamics simulations to improve the computational efficiency in many-queryscenarios or to perform real-time simulations. However, the snapshots of themethod require to be collected from the same discretisation, which is astraightforward process for physical parameters, but becomes challenging forgeometrical problems, especially for those domains featuring unparameterisedand unique shapes, e.g. patient-specific geometries. In this work, adata-driven surrogate model is proposed for the efficient prediction of bloodflow simulations on similar but distinct domains. The proposed surrogate modelleverages group surface registration to parameterise those shapes andformulates corresponding hemodynamics information into geometry-informedsnapshots by the diffeomorphisms constructed between a reference domain andoriginal domains. A non-intrusive reduced-order model for geometricalparameters is subsequently constructed using proper orthogonal decomposition,and a radial basis function interpolator is trained for predicting the reducedcoefficients of the reduced-order model based on compressed geometricalparameters of the shape. Two examples of blood flowing through a stenosis and abifurcation are presented and analysed. The proposed surrogate modeldemonstrates its accuracy and efficiency in hemodynamics prediction and showsits potential application toward real-time simulation or uncertaintyquantification for complex patient-specific scenarios.

Data-driven reduced-order modelling for blood flow simulations with geometry-informed snapshots