Stiff-PDEs and Physics-Informed Neural Networks
نویسندگان
چکیده
Abstract In recent years, physics-informed neural networks (PINN) have been used to solve stiff-PDEs mostly in the 1D and 2D spatial domain. PINNs still experience issues solving 3D problems, especially, problems with conflicting boundary conditions at adjacent edges corners. These discontinuous solutions corners that are difficult learn for a continuous activation function. this review paper, we investigated various PINN frameworks designed stiff-PDEs. We took two heat conduction (2D 3D) solution as test cases. these number of frameworks, discussed analysed results against FEM solution. It appears provide more general platform parameterisation compared conventional solvers. Thus, problem parametric conductivity geometry separately. also discuss challenges associated identify areas further investigation.
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ژورنال
عنوان ژورنال: Archives of Computational Methods in Engineering
سال: 2023
ISSN: ['1886-1784', '1134-3060']
DOI: https://doi.org/10.1007/s11831-023-09890-4