Grid Convolution

Grid convolution is a technique extending convolutional neural networks to handle data structured on regular grids, offering an alternative to graph-based methods for processing spatial information. Current research focuses on applying grid convolution in diverse fields, including image super-resolution (using architectures like Implicit Grid Convolution) and spatiotemporal forecasting (e.g., sea surface temperature prediction), often comparing its performance against traditional convolutional or graph neural network approaches. This approach shows promise in improving efficiency and accuracy in various applications by leveraging the inherent structure of gridded data, leading to more efficient model training and potentially better predictions.