Robust Physics-Informed Neural Network Approach for Estimating Heterogeneous Elastic Properties from Noisy Displacement Data.

ABSTRACT: Accurately estimating spatially heterogeneous elasticity parameters, particularly Young's modulus and Poisson's ratio, from noisy displacement measurements remains a significant challenge in inverse elasticity problems. Existing inverse estimation techniques are often limited by instability, high noise sensitivity, and difficulties in recovering the absolute scale of Young's modulus. This work presents a novel Inverse Elasticity Physics-Informed Neural Network (IE-PINN) to robustly reconstruct heterogeneous elasticity distributions from noisy displacement data based on the principles of linear elasticity. The IE-PINN incorporates three distinct neural network architectures, each dedicated to modeling displacement fields, strain fields, and elasticity distributions. This approach significan

SUBMITTER: Srikitrungruang T 

PROVIDER: S-EPMC12752583 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature