Estimation of in vivo constitutive parameters of the aortic wall using a machine learning approach

DOI10.1016/j.cma.2018.12.030zbMath1440.74229OpenAlexW2950521115WikidataQ92503703 ScholiaQ92503703MaRDI QIDQ1987805

Minliang Liu, Liang Liang, Wei Sun

Publication date: 16 April 2020

Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc6544444

zbMATH Keywords

neural networkmachine learningconstitutive parameter estimation

Mathematics Subject Classification ID

Biomechanical solid mechanics (74L15) Physiological flows (76Z05) Computer-aided design (modeling of curves and surfaces) (65D17) Numerical and other methods in solid mechanics (74S99)

Related Items (7)

A Novel Parameters’ Identification Procedure for Aortic Walls Based on Hybrid Artificial Intelligence Approaches ⋮ Learning Invariant Representation of Multiscale Hyperelastic Constitutive Law from Sparse Experimental Data ⋮ Synergistic integration of deep neural networks and finite element method with applications of nonlinear large deformation biomechanics ⋮ Estimation of left ventricular parameters based on deep learning method ⋮ Machine learning meta-models for fast parameter identification of the lattice discrete particle model ⋮ A generic physics-informed neural network-based constitutive model for soft biological tissues ⋮ Evaluation of equivalent material properties of reinforced composites by a novel smoothed rebar element technique

Uses Software

• LIBSVM
• ImageNet
• DeepFace
• Adam
• GNMT
• AlexNet
• Deep Speech

Cites Work

• Unnamed Item
• Unnamed Item
• Neural networks for computing in fracture mechanics. Methods and prospects of applications
• Determination of constitutive properties from spherical indentation data using neural networks. I: The case of pure kinematic hardening in plasticity laws. II: Plasticity with nonlinear isotropic and kinematic hardening
• Statistical Pattern Recognition
• Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
• An exponential constitutive model excluding fibres under compression: Application to extension–inflation of a residually stressed carotid artery
• A new constitutive framework for arterial wall mechanics and a comparative study of material models

This page was built for publication: Estimation of in vivo constitutive parameters of the aortic wall using a machine learning approach