Zhuofan Li1, Daniel H. Pak2, James S. Duncan2, Liang Liang3, Minliang Liu1,*
CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.073757
- 29 January 2026
Abstract Patient-specific finite element analysis (FEA) is a promising tool for noninvasive quantification of cardiac and vascular structural mechanics in vivo. However, inverse material property identification using FEA, which requires iteratively solving nonlinear hyperelasticity problems, is computationally expensive which limits the ability to provide timely patient-specific insights to clinicians. In this study, we present an inverse material parameter identification strategy that integrates deep neural networks (DNNs) with FEA, namely inverse DNN-FEA. In this framework, a DNN encodes the spatial distribution of material parameters and effectively regularizes the inverse solution, which aims to reduce susceptibility to local optima… More >