This study investigates the problem of modeling the deformation of square thin plates under the influence of their own weight. The main objective is to conduct a comparative analysis of the accuracy and efficiency of traditional methods and a modern artificial intelligence approach, Physics-Informed Neural Network (PINN) algorithms, in calculating the bending of plates with rigidly clamped and simply supported edges. During the research, the fundamental differential equations of mechanics and boundary conditions were integrated into the loss function of the neural network to determine the displacement field of the plate.
| Mualliflar | Salayev, Alisher, Nuraliev, Faxriddin, Safarov, Shohruh, Okboyev, Olti |
|---|---|
| Jurnal | Al-Farg'oniy avlodlari |
| Nashr sanasi | 2026-07-19 |
| Son | 3 |
| Betlar | 36-43 |
| Til | Rus |
Xususiy hosilali differensial tenglamalar, neyron tarmoq, PINN, yupqa plastinka deformatsiyasi, chegaraviy shartlar, qiyosiy tahlil., Eigenvalue differential equations, neural network, PINN, thin plate deformation, boundary conditions, comparative analysis.
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