Coarse Grid Operator

Coarse grid operators are essential components of numerical methods, particularly multigrid solvers, used to efficiently solve complex partial differential equations. Current research focuses on developing more efficient and robust coarse grid operators, often employing machine learning techniques like neural networks to reduce computational complexity while maintaining solution accuracy, particularly for challenging problems like anisotropic PDEs. This work is driven by a need for improved reproducibility and reusability of scientific workflows, leading to the development of open-source frameworks that facilitate the sharing and reuse of these operators across diverse scientific applications. Ultimately, advancements in coarse grid operator design promise to accelerate scientific discovery and improve the efficiency of simulations across various fields.