Deep learning acceleration of total Lagrangian explicit dynamics for soft tissue mechanics
type: Item
instance of: scholarly article
title: Deep learning acceleration of total Lagrangian explicit dynamics for soft tissue mechanics
zbMATH Open document ID: 1441.74263
author: Yanyan Li
published in: Computer Methods in Applied Mechanics and Engineering
publication date: 2020-04-24T00:00:00Z
Mathematics Subject Classification ID: 74S05
Mathematics Subject Classification ID: 65D18
Mathematics Subject Classification ID: 74L15
zbMATH DE Number: 7193216
zbMATH Keywords: data-driven computational modeling
zbMATH Keywords: soft tissue biomechanics
zbMATH Keywords: deep learning
zbMATH Keywords: finite element method
zbMATH Keywords: total Lagrangian explicit dynamics
describes a project that uses: Adam
describes a project that uses: Keras
describes a project that uses: FEAPpv
describes a project that uses: FEBio
describes a project that uses: TensorFlow
MaRDI profile type: Publication
full work available at URL: j.cma.2019.112628
OpenAlex ID: W2975025423
cites work: Q5177422
cites work: Total Lagrangian explicit dynamics finite element algorithm for computing soft tissue deformation
cites work: Highly scalable parallel domain decomposition methods with an application to biomechanics
cites work: Q5270493
cites work: Why does deep and cheap learning work so well?
cites work: Hidden physics models: machine learning of nonlinear partial differential equations
cites work: Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
cites work: Computational mechanics enhanced by deep learning
cites work: Constitutive modelling of passive myocardium: a structurally based framework for material characterization
Wikidata QID: Q127189844
DOI: 10.1016/J.CMA.2019.112628
Recommended article: Deep learning-accelerated computational framework based on physics informed neural network for the solution of linear elasticity
Recommended article: Computational mechanics enhanced by deep learning
Recommended article: Data-driven tissue mechanics with polyconvex neural ordinary differential equations
Recommended article: A deep learning energy method for hyperelasticity and viscoelasticity
Recommended article: Total Lagrangian explicit dynamics finite element algorithm for computing soft tissue deformation
Recommended article: A generic physics-informed neural network-based constitutive model for soft biological tissues
Recommended article: Computational Mechanics with Deep Learning
Recommended article: Probabilistic deep learning for real-time large deformation simulations
Recommended article: Synergistic integration of deep neural networks and finite element method with applications of nonlinear large deformation biomechanics
Recommended article: DeepBND: a machine learning approach to enhance multiscale solid mechanics
description: scientific article; zbMATH DE number 7193216