A neural network-based approach for bending analysis of strain gradient nanoplates

DOI10.1016/j.enganabound.2022.10.017zbMath1521.82027OpenAlexW4308951978MaRDI QIDQ6044720

C. A. Yan, Riccardo Vescovini, Nicholas Fantuzzi

Publication date: 22 May 2023

Published in: Engineering Analysis with Boundary Elements (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.enganabound.2022.10.017

zbMATH Keywords

parameter identificationextreme learning machinenanoplatesbending analysisstrain gradient theoryphysics-informed neural networks

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Statistical mechanics of nanostructures and nanoparticles (82D80) Finite element, Galerkin and related methods applied to problems in statistical mechanics (82M10)

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Cites Work

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• Buckling analysis of functionally graded microbeams based on the strain gradient theory
• Analysis of micro-sized beams for various boundary conditions based on the strain gradient elasticity theory
• Static, stability and dynamic analysis of gradient elastic flexural Kirchhoff plates
• On the role of gradients in the localization of deformation and fracture
• Experiments and theory in strain gradient elasticity.
• Bending analysis of FG plates using a general third-order plate theory with modified couple stress effect and MLPG method
• Analytical solution for strain gradient elastic Kirchhoff rectangular plates under transverse static loading
• Couple stress based strain gradient theory for elasticity
• A meshfree method with gradient smoothing for free vibration and buckling analysis of a strain gradient thin plate
• A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics
• Deep autoencoder based energy method for the bending, vibration, and buckling analysis of Kirchhoff plates with transfer learning
• On the analytical and meshless numerical approaches to mixture stress gradient functionally graded nano-bar in tension
• On the static, vibration, and transient responses of micro-plates made of materials with different microstructures
• Optimization of a radial point interpolation meshless strategy for strain gradient nanoplates
• Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems
• Strain gradient differential quadrature Kirchhoff plate finite element with the \(C^2\) partial compatibility
• Isogeometric analysis for sixth-order boundary value problems of gradient-elastic Kirchhoff plates
• 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
• Strain gradient beam element
• A microstructure-dependent sinusoidal plate model based on the strain gradient elasticity theory
• Effects of couple-stresses in linear elasticity
• The mechanically based non-local elasticity: an overview of main results and future challenges
• Radial Point Interpolation Method for Isotropic Nanoplates in Bending Using Strain Gradient Theory

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