Deep learning acceleration of total Lagrangian explicit dynamics for soft tissue mechanics

DOI10.1016/j.cma.2019.112628zbMath1441.74263OpenAlexW2975025423WikidataQ127189844 ScholiaQ127189844MaRDI QIDQ1989089

Yanyan Li

Publication date: 24 April 2020

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

Full work available at URL: https://doi.org/10.1016/j.cma.2019.112628

zbMATH Keywords

finite element methoddeep learningsoft tissue biomechanicsdata-driven computational modelingtotal Lagrangian explicit dynamics

Mathematics Subject Classification ID

Finite element methods applied to problems in solid mechanics (74S05) Numerical aspects of computer graphics, image analysis, and computational geometry (65D18) Biomechanical solid mechanics (74L15)

Related Items (8)

An intelligent nonlinear meta element for elastoplastic continua: deep learning using a new time-distributed residual U-net architecture ⋮ An explicit total Lagrangian fragile points method for finite deformation of hyperelastic materials ⋮ A massively parallel explicit solver for elasto-dynamic problems exploiting octree meshes ⋮ A machine learning framework for accelerating the design process using CAE simulations: an application to finite element analysis in structural crashworthiness ⋮ Deep learning for model order reduction of multibody systems to minimal coordinates ⋮ Editorial: Special issue on uncertainty quantification, machine learning, and data-driven modeling of biological systems ⋮ Constrained finite element method for runtime modeling of soft tissue deformation ⋮ A Bayesian multiscale CNN framework to predict local stress fields in structures with microscale features

Uses Software

• FEAPpv
• FEBio
• TensorFlow
• Keras
• Adam

Cites Work

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• Why does deep and cheap learning work so well?
• Hidden physics models: machine learning of nonlinear partial differential equations
• Computational mechanics enhanced by deep learning
• Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
• Total Lagrangian explicit dynamics finite element algorithm for computing soft tissue deformation
• Constitutive modelling of passive myocardium: a structurally based framework for material characterization
• Highly scalable parallel domain decomposition methods with an application to biomechanics

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