Random Forest, XGBoost, and LightGBM surrogate models predict third-order Ogden corneal parameters from apex displacement data, evaluated with a physics-informed stress-stretch …
The Holzapfel-Gasser-Ogden anisotropic hyperelastic model is integrated into the NL-SBFEM framework and validated against ABAQUS with errors below 2%.
A neural network-based inverse model was developed to predict Ogden material parameters from corneal apex displacement data.
The objective of this research is to develop and validate a deep learning model for predicting the hypertrophy index from histological microscopy images of pulmonary circulation …
In this study, six implant stem designs were considered, and finite element analysis was performed on these six models.
A three-dimensional model of the human head was extracted using medical images. Eight loading scenarios were considered to compare different impact characteristics.