Synergistic integration of deep neural networks and finite element method with applications of nonlinear large deformation biomechanics

DOI10.1016/j.cma.2023.116347OpenAlexW4386147335MaRDI QIDQ6084492

Minliang Liu, Wei Sun, John Elefteriades, Liang Liang

Publication date: 6 November 2023

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.2023.116347

zbMATH Keywords

finite element methodinverse methoddeep neural networkaortic aneurysm

Mathematics Subject Classification ID

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

Cites Work

• On the limited memory BFGS method for large scale optimization
• Smart finite elements: a novel machine learning application
• Estimation of in vivo constitutive parameters of the aortic wall using a machine learning approach
• Smart constitutive laws: inelastic homogenization through machine learning
• Finite electro-elasticity with physics-augmented neural networks
• Simulation of the 3D hyperelastic behavior of ventricular myocardium using a finite-element based neural-network approach
• Data-driven tissue mechanics with polyconvex neural ordinary differential equations
• General finite-element framework of the virtual fields method in nonlinear elasticity
• Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
• Machine learning constitutive models of elastomeric foams
• Intelligent stiffness computation for plate and beam structures by neural network enhanced finite element analysis

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