Generative Hyperelasticity with Physics-Informed Probabilistic Diffusion Fields.

ABSTRACT: Many natural materials exhibit highly complex, nonlinear, anisotropic, and heterogeneous mechanical properties. Recently, it has been demonstrated that data-driven strain energy functions possess the flexibility to capture the behavior of these complex materials with high accuracy while satisfying physics-based constraints. However, most of these approaches disregard the uncertainty in the estimates and the spatial heterogeneity of these materials. In this work, we leverage recent advances in generative models to address these issues. We use as building block neural ordinary equations (NODE) that - by construction - create polyconvex strain energy functions, a key property of realistic hyperelastic material models. We combine this approach with probabilistic diffusion models to generate ne

SUBMITTER: Tac V 

PROVIDER: S-EPMC12052335 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature