A stepwise physics‐informed neural network for solving large deformation problems of hypoelastic materials

DOI10.1002/nme.7323zbMath1524.74466OpenAlexW4383106088MaRDI QIDQ6082603

Mengkai Lu, Unnamed Author, Unnamed Author

Publication date: 30 November 2023

Published in: International Journal for Numerical Methods in Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/nme.7323

zbMATH Keywords

large deformationnonlinear analysisdeep learninghypoelastic materialphysics-informed neural network

Mathematics Subject Classification ID

Numerical and other methods in solid mechanics (74S99)

Cites Work

• Unnamed Item
• Unnamed Item
• Multilayer feedforward networks are universal approximators
• The neural particle method - an updated Lagrangian physics informed neural network for computational fluid dynamics
• A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics
• Physics-informed neural networks for solving forward and inverse flow problems via the Boltzmann-BGK formulation
• The mixed deep energy method for resolving concentration features in finite strain hyperelasticity
• A general neural particle method for hydrodynamics modeling
• Physics informed neural networks for continuum micromechanics
• Physics-informed neural networks for high-speed flows
• A composite neural network that learns from multi-fidelity data: application to function approximation and inverse PDE problems
• A physics-guided neural network framework for elastic plates: comparison of governing equations-based and energy-based approaches
• A nonlocal physics-informed deep learning framework using the peridynamic differential operator
• An energy approach to the solution of partial differential equations in computational mechanics via machine learning: concepts, implementation and applications
• Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
• A deep learning energy method for hyperelasticity and viscoelasticity
• ModalPINN: an extension of physics-informed neural networks with enforced truncated Fourier decomposition for periodic flow reconstruction using a limited number of imperfect sensors
• Finite rotation effects in numerical integration of rate constitutive equations arising in large-deformation analysis

This page was built for publication: A stepwise physics‐informed neural network for solving large deformation problems of hypoelastic materials