With the advancement of materials science, the modeling of cross-ply composite laminates using traditional numerical methods like the finite element method and analytical solutions has become increasingly complex, especially when dealing with complex boundary conditions and material distributions. These traditional approaches struggle to adequately capture the intricate behavior of laminated plates, creating a need for reliable and innovative solutions. However, research on applying physics-informed neural networks (PINN) to the static analysis of orthotropic composite plates remains insufficient.A novelty solution for orthotropic composite plate based on physics informed neural network
September 15, 2025
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With the advancement of materials science, the modeling of cross-ply composite laminates using traditional numerical methods like the finite element method and analytical solutions has become increasingly complex, especially when dealing with complex boundary conditions and material distributions. These traditional approaches struggle to adequately capture the intricate behavior of laminated plates, creating a need for reliable and innovative solutions. However, research on applying physics-informed neural networks (PINN) to the static analysis of orthotropic composite plates remains insufficient.
