Learning incomplete factorization preconditioners for GMRES
In this paper, we develop a data-driven approach to generate incomplete LU factorizations
of large-scale sparse matrices. The learned approximate factorization is utilized as a …
of large-scale sparse matrices. The learned approximate factorization is utilized as a …
Convergence Framework of Deep Learning-based Hybrid Iterative Methods and the Application to Designing a Fourier Neural Solver for Parametric PDEs
C Cui, K Jiang, Y Liu, S Shu - arXiv preprint arXiv:2408.08540, 2024 - arxiv.org
Recently, deep learning-based hybrid iterative methods (DL-HIM) have emerged as a
promising approach for designing fast neural solvers to tackle large-scale sparse linear …
promising approach for designing fast neural solvers to tackle large-scale sparse linear …
Momentum-Accelerated Richardson (m) and Their Multilevel Neural Solvers
Z Wang, Y Liu, C Cui, S Shu - arXiv preprint arXiv:2412.08076, 2024 - arxiv.org
Recently, designing neural solvers for large-scale linear systems of equations has emerged
as a promising approach in scientific and engineering computing. This paper first introduce …
as a promising approach in scientific and engineering computing. This paper first introduce …