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References from A PINN-based framework for the analysis of multiple plate theories. Local targets link to admitted publications; unresolved targets remain external evidence.
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
10.1016/j.jcp.2018.10.045 · 2019 · External reference
Advancements in physics-informed neural networks for laminated composites: A comprehensive review
10.3390/math13010017 · 2024 · External reference
Physics-based machine learning for modeling of laminated composite plates based on refined zigzag theory
10.1007/s00419-025-02816-5 · 2025 · External reference
A physics-informed neural network technique based on a modified loss function for computational 2d and 3d solid mechanics
10.1007/s00466-022-02252-0 · 2023 · External reference
A review on the applications of artificial neural network techniques for accelerating finite element analysis
10.1016/j.compstruc.2025.107698 · 2025 · External reference
Understanding and mitigating gradient flow pathologies in physics-informed neural networks
10.1137/20m1318043 · 2021 · External reference
A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics
10.1016/j.cma.2021.113741 · 2021 · External reference
Physics-informed neural networks for shell structures
10.1016/j.euromechsol.2022.104849 · 2023 · External reference
Physics-informed neural network for solution of nonlinear differential equations
2024 · External reference
Physics-informed neural network for bending and free vibration analysis of three-dimensional functionally graded porous beam resting on elastic foundation
10.1007/s00366-023-01799-7 · 2024 · External reference
A physics-guided neural network framework for elastic plates: Comparison of governing equations-based and energy-based approaches
10.1016/j.cma.2021.113933 · 2021 · External reference
An energy approach to the solution of partial differential equations in computational mechanics via machine learning: Concepts, implementation and applications
10.1016/j.cma.2019.112790 · 2020 · External reference
A physics-informed neural network framework for laminated composite plates under bending
10.1016/j.tws.2025.113014 · 2025 · External reference
A complete physics-informed neural network-based framework for structural topology optimization
10.1016/j.cma.2023.116401 · 2023 · External reference
Physics-constrained deep learning approach for solving inverse problems in composite laminated plates
10.1016/j.compstruct.2024.118514 · 2024 · External reference
Free vibration analysis of porous functionally graded curved beams using an improved first-order shear deformation theory
10.1142/s0219455427503032 · 2026 · External reference
Improved timoshenko beam theory for mechanical and thermal flexural behavior of porous cross-ply laminated beams
2025 · External reference
On the thermal bending behavior of porous cross-ply laminated plates using an improved first-order shear deformation theory
10.1007/s00707-025-04369-8 · 2025 · External reference
On the bending behavior of porous functionally graded plates under hygro-thermo-mechanical loads
2025 · External reference
Free vibration analysis of nonlinearly dispersed FG-CNTRC beams on Kerr substrate using an improved FSDT
2025 · External reference
An improved first-order shear deformation theory for wave propagation analysis in FG-CNTRC beams resting on a viscoelastic substrate
10.1142/s0219455425500105 · 2025 · External reference
Dynamic response of imperfect functionally graded plates: Impact of graded patterns and viscoelastic foundation
2024 · External reference
Free vibrational characteristics of various imperfect FG beam via a novel integral Timoshenko’s theory
10.1007/s00707-024-04046-2 · 2024 · External reference
Deep autoencoder based energy method for the bending, vibration, and buckling analysis of Kirchhoff plates with transfer learning
10.1016/j.euromechsol.2021.104225 · 2021 · External reference
A novelty solution for orthotropic composite plate based on physics informed neural network
10.1007/s11709-025-1178-3 · 2025 · External reference
Solving linear elasticity problems using physics-informed neural networks
2024 · External reference
Shear deformation in heterogeneous anisotropic plates
10.1115/1.3408654 · 1970 · External reference
Theories and finite elements for multilayered plates and shells:A unified compact formulation with numerical assessment and benchmarking
10.1007/bf02736224 · 2003 · External reference
Sympy: symbolic computing in python
10.7717/peerj-cs.103 · 2017 · External reference
Pytorch: An imperative style, high-performance deep learning library
2019 · External reference
Unresolved reference
2003 · External reference
10.5244/c.34.191
10.5244/c.34.191 · External reference
MATLAB implementation of physics informed deep neural networks for forward and inverse structural vibration problems
10.3389/arc.2024.13194 · 2024 · External reference
Adam: A method for stochastic optimization
2015 · External reference
A comparison of three methods for selecting values of input variables in the analysis of output from a computer code
1979 · External reference
SGDR: Stochastic gradient descent with warm restarts
2017 · External reference
A nonlinear finite element analysis of laminated shells with a damage model
2024 · External reference
Nonlinear finite element damage analysis of laminated shells by carrera unified formulation
10.1016/j.compstruct.2024.118494 · 2024 · External reference