Том: Computational Science and Its Applications - ICCSA 2025 Workshops. 25th International Conference (Istanbul, Turkey, June 30 - July 3, 2025). Proceedings, Part III
Том: 15888
, Год издания: 2025
Многотомное издание: Computational Science and Its Applications - ICCSA 2025 Workshops. 25th International Conference (Istanbul, Turkey, June 30 - July 3, 2025). Proceedings, Part III
Страницы: 376-387
Аннотация
In this paper, we present an attempt to construct a preconditioner based on the machine learning to solve Poisson equation. We use the Conjugate Gradient method. To precondition the algorithm we suggest approximating the inverse Laplace operator with using the U-Net. We consider the supervised learning where the vector of unknowns and right-hand sides are known; thus, we use the relative L2 error as the loss function of the network training. We illustrate that U-Net with five convolutional layers provide insuffient accuracy of inverse Laplace operator approximation, so that the constructed conjugate gradient method stabilizes and possesses irreducible residual.