Coarse Grid

Coarse grid methods aim to efficiently simulate complex systems by reducing computational cost through lower resolution grids, but this simplification introduces inaccuracies. Current research focuses on mitigating these errors using machine learning, particularly neural networks and physics-informed neural operators, to learn corrections or directly predict fine-grid behavior from coarse-grid simulations. These approaches show promise in diverse fields like climate modeling and computational fluid dynamics, offering significant speedups while maintaining acceptable accuracy for long-term predictions and rare event quantification. The ultimate goal is to improve the accuracy and efficiency of simulations across various scientific and engineering disciplines.