Synthetic FEM Dataset for Inverse Identification of Breast Tissue Elastic Properties

This dataset contains synthetic finite element simulation data generated for the study "Finite Element–Machine Learning Surrogates for Inverse Identification of Breast Tissue Elastic Properties: Robustness, Sparse Observations, and Extrapolation Analysis". The dataset comprises 1,000 finite element simulations of breast compression generated using FEBio. The simulations vary the Young's modulus of skin, adipose tissue, and glandular tissue within predefined parameter ranges. The resulting displacement fields, together with the prescribed compression displacement and corresponding material parameters, were used to train and evaluate machine-learning surrogate models for inverse identification of tissue elastic properties. The dataset supports the baseline prediction experiments, learning-curve analysis, noise-sensitivity analysis, sparse-observation experiments, leave-one-range-out extrapolation analysis, and naive baseline comparison reported in the associated manuscript. The corresponding FEBio template, computational workflow, preprocessing scripts, machine-learning experiments, and analysis code are archived separately on Zenodo and available through the associated software repository.