A Novel Parameters’ Identification Procedure for Aortic Walls Based on Hybrid Artificial Intelligence Approaches

DOI10.1142/s0219876221430064OpenAlexW3205562838MaRDI QIDQ6048284

No author found.

Publication date: 10 October 2023

Published in: International Journal of Computational Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1142/s0219876221430064

zbMATH Keywords

finite element methodrandom forestparameters identificationaortic wallsGrid Search \& Random Search

Mathematics Subject Classification ID

Mechanics of deformable solids (74-XX) Biology and other natural sciences (92-XX)

Cites Work

• Unnamed Item
• Unnamed Item
• Forward and inverse structural uncertainty propagations under stochastic variables with arbitrary probability distributions
• Estimation of in vivo constitutive parameters of the aortic wall using a machine learning approach
• Computational Inverse Techniques in Nondestructive Evaluation
• A Neural Element Method
• Tubenet: A Special Trumpetnet for Explicit Solutions to Inverse Problems
• A Novel Constitutive Parameters Identification Procedure for Hyperelastic Skeletal Muscles Using Two-Way Neural Networks
• Development of a Software Package of Smoothed Finite Element Method (S-FEM) for Solid Mechanics Problems
• Random forests