Ising on the graph: Task-specific graph subsampling via the Ising model
Reducing a graph while preserving its overall structure is an important problem with many
applications. Typically, reduction approaches either remove edges (sparsification) or merge …
applications. Typically, reduction approaches either remove edges (sparsification) or merge …
Neural incomplete factorization: learning preconditioners for the conjugate gradient method
Finding suitable preconditioners to accelerate iterative solution methods, such as the
conjugate gradient method, is an active area of research. In this paper, we develop a …
conjugate gradient method, is an active area of research. In this paper, we develop a …
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 …
Advancing Heatwave Forecasting via Distribution Informed-Graph Neural Networks (DI-GNNs): Integrating Extreme Value Theory with GNNs
Heatwaves, prolonged periods of extreme heat, have intensified in frequency and severity
due to climate change, posing substantial risks to public health, ecosystems, and …
due to climate change, posing substantial risks to public health, ecosystems, and …
Deep Learning-Enhanced Preconditioning for Efficient Conjugate Gradient Solvers in Large-Scale PDE Systems
R Li, S Wang, C Wang - arxiv preprint arxiv:2412.07127, 2024 - arxiv.org
Preconditioning techniques are crucial for enhancing the efficiency of solving large-scale
linear equation systems that arise from partial differential equation (PDE) discretization …
linear equation systems that arise from partial differential equation (PDE) discretization …