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, where aortic shapes are generated from an initial cohort of patients. The conditioning variable is a latent geometric representation defined by a set of centerline points and the radii of the corres...
| Authors | Sarah Katz, Francesco Romor, Jia-Jie Zhu et al. |
|---|---|
| Primary category | stat.ML |
| Published | 30 March 2026 |
| Last updated at source | 30 March 2026 |
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