  Parametric reduced-order modelling often serves as a surrogate method for
hemodynamics simulations to improve the computational efficiency in many-query
scenarios or to perform real-time simulations. However, the snapshots of the
method require to be collected from the same discretisation, which is a
straightforward process for physical parameters, but becomes challenging for
geometrical problems, especially for those domains featuring unparameterised
and unique shapes, e.g. patient-specific geometries. In this work, a
data-driven surrogate model is proposed for the efficient prediction of blood
flow simulations on similar but distinct domains. The proposed surrogate model
leverages group surface registration to parameterise those shapes and
formulates corresponding hemodynamics information into geometry-informed
snapshots by the diffeomorphisms constructed between a reference domain and
original domains. A non-intrusive reduced-order model for geometrical
parameters is subsequently constructed using proper orthogonal decomposition,
and a radial basis function interpolator is trained for predicting the reduced
coefficients of the reduced-order model based on compressed geometrical
parameters of the shape. Two examples of blood flowing through a stenosis and a
bifurcation are presented and analysed. The proposed surrogate model
demonstrates its accuracy and efficiency in hemodynamics prediction and shows
its potential application toward real-time simulation or uncertainty
quantification for complex patient-specific scenarios.
