LDDMM 相交插值:用于血动力学领域不确定性量化的应用程序

LDDMM stochastic interpolants: an application to domain uncertainty quantification in hemodynamics

We introduce a novel conditional stochastic interpolant framework for generative modeling of three-dimensional shapes. The method builds on a recent LDDMM-based registration approach to learn the conditional drift between geometries. By leveraging the resulting pull-back and push-forward operators, we extend this formulation beyond standard Cartesian grids to complex shapes and random variables defined on distinct domains. We present an application in the context of cardiovascular simulations, w