An introduction to programming physics-informed neural network-based computational solid mechanics (Q6564385)

scientific article; zbMATH DE number 7873505

Language	Label	Description	Also known as
English	An introduction to programming physics-informed neural network-based computational solid mechanics	scientific article; zbMATH DE number 7873505

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instance of

scholarly article

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title

An introduction to programming physics-informed neural network-based computational solid mechanics (English)

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author

Jinshuai Bai

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Hyogu Jeong

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C. P. Batuwatta-Gamage

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Shusheng Xiao

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Qingxia Wang

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Charith Malinga Rathnayaka

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International Journal of Computational Methods

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publication date

1 July 2024

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zbMATH Keywords

physics-informed neural network

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computational mechanics

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deep learning

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MaRDI profile type

Publication

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cites work

A data-driven smoothed particle hydrodynamics method for fluids

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A physics-informed neural network technique based on a modified loss function for computational 2D and 3D solid mechanics

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A general neural particle method for hydrodynamics modeling

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Q4558473

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Flow over an espresso cup: inferring 3-D velocity and pressure fields from tomographic background oriented Schlieren via physics-informed neural networks

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Local extreme learning machines and domain decomposition for solving linear and nonlinear partial differential equations

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A physics-informed variational DeepONet for predicting crack path in quasi-brittle materials

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A nonlocal physics-informed deep learning framework using the peridynamic differential operator

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A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics

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Physics informed neural networks for continuum micromechanics

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Multilayer feedforward networks are universal approximators

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\textit{hp}-VPINNs: variational physics-informed neural networks with domain decomposition

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A physics-guided neural network framework for elastic plates: comparison of governing equations-based and energy-based approaches

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PPINN: parareal physics-informed neural network for time-dependent PDEs

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Remark on “algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound constrained optimization”

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Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

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Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations

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An energy approach to the solution of partial differential equations in computational mechanics via machine learning: concepts, implementation and applications

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On the eigenvector bias of Fourier feature networks: from regression to solving multi-scale PDEs with physics-informed neural networks

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The neural particle method - an updated Lagrangian physics informed neural network for computational fluid dynamics

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B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data

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Predicting the Pore-Pressure and Temperature of Fire-Loaded Concrete by a Hybrid Neural Network

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Deep autoencoder based energy method for the bending, vibration, and buckling analysis of Kirchhoff plates with transfer learning

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Identifiers

DOI

10.1142/s0219876223500135

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Mathematics Subject Classification ID

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Sitelinks

Mathematics(1 entry)

mardi Publication:6564385