Shape-informed cardiac mechanics surrogates in data-scarce regimes via geometric encoding and generative augmentation

This record contains the data accompanying the paper Shape-informed cardiac mechanics surrogates in data-scarce regimes via geometric encoding and generative augmentation. The dataset supports the shape model component of the work: a neural implicit shape model of the left ventricle (SDF-SM) combined with a classical PCA shape model (PCA-SM). The archive contains normalised left ventricular meshes for 44 patient geometries together with 1000 synthetic geometries generated by the trained SDF-SM and their latent codes. Related links: Preprint: https://arxiv.org/abs/2602.20306 Code repository: https://gitlab.com/davidecarrara98/shape-informed-cardiac-mechanics-surrogate