Physics-informed graph neural network emulation of soft-tissue mechanics

DOI10.1016/j.cma.2023.116351MaRDI QIDQ6194151

David R. Dalton, Hao Gao, Dirk Husmeier

Publication date: 14 February 2024

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

zbMATH Keywords

soft-tissue mechanicsgraph neural networksphysics-informed machine learning

Mathematics Subject Classification ID

Biology and other natural sciences (92-XX) Quantum theory (81-XX)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• The numerical solution of linear ordinary differential equations by feedforward neural networks
• The Deep Ritz Method: a deep learning-based numerical algorithm for solving variational problems
• Emulation of cardiac mechanics using graph neural networks
• Physics-informed neural network simulation of multiphase poroelasticity using stress-split sequential training
• Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
• Neural algorithm for solving differential equations
• Deep energy method in topology optimization applications
• Solving PDEs in Python
• Relu Deep Neural Networks and Linear Finite Elements
• Constitutive modelling of passive myocardium: a structurally based framework for material characterization
• Physical Systems with Random Uncertainties: Chaos Representations with Arbitrary Probability Measure
• F‐bar‐based linear triangles and tetrahedra for finite strain analysis of nearly incompressible solids. Part I: formulation and benchmarking

This page was built for publication: Physics-informed graph neural network emulation of soft-tissue mechanics