Physics-informed UNets for discovering hidden elasticity in heterogeneous materials.

ABSTRACT: Soft biological tissues often have complex mechanical properties due to variation in structural components. In this paper, we develop a novel UNet-based neural network model for inversion in elasticity (El-UNet) to infer the spatial distributions of mechanical parameters from strain maps as input images, normal stress boundary conditions, and domain physics information. We show superior performance - both in terms of accuracy and computational cost - by El-UNet compared to fully-connected physics-informed neural networks in estimating unknown parameters and stress distributions for isotropic linear elasticity. We characterize different variations of El-UNet and propose a self-adaptive spatial loss weighting approach. To validate our inversion models, we performed various finite-element sim

SUBMITTER: Kamali A 

PROVIDER: S-EPMC10842800 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature