Emulation of cardiac mechanics using graph neural networks
type: Item
instance of: scholarly article
title: Emulation of cardiac mechanics using graph neural networks
author: David R. Dalton
author: Hao Gao
author: Dirk Husmeier
published in: Computer Methods in Applied Mechanics and Engineering
publication date: 2022-11-11T00:00:00Z
Mathematics Subject Classification ID: 92C10
Mathematics Subject Classification ID: 62P10
Mathematics Subject Classification ID: 68T07
zbMATH DE Number: 7615002
zbMATH Keywords: cardiac mechanics
zbMATH Keywords: emulation
zbMATH Keywords: graph neural networks
describes a project that uses: Scikit
describes a project that uses: JAX
describes a project that uses: Adam
describes a project that uses: Flax
describes a project that uses: GitHub
MaRDI profile type: Publication
full work available at URL: j.cma.2022.115645
OpenAlex ID: W4302425293
cites work: Q5396710
cites work: Bayesian calibration of computer models. (With discussion)
cites work: Q5030377
cites work: Physical Systems with Random Uncertainties: Chaos Representations with Arbitrary Probability Measure
cites work: Integrated heart -- coupling multiscale and multiphysics models for the simulation of the cardiac function
cites work: Q3514988
cites work: A comparison of neural network architectures for data-driven reduced-order modeling
cites work: Solving PDEs in Python
cites work: State space reconstruction in the presence of noise
cites work: Constitutive modelling of passive myocardium: a structurally based framework for material characterization
cites work: Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
cites work: Simulation of the 3D hyperelastic behavior of ventricular myocardium using a finite-element based neural-network approach
cites work: Physics-informed graph neural Galerkin networks: a unified framework for solving PDE-governed forward and inverse problems
DOI: 10.1016/J.CMA.2022.115645
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description: scientific article; zbMATH DE number 7615002