Convolution Operator

The convolution operator, a fundamental building block in many neural networks, aims to extract features from data by applying a weighted sum across neighboring elements. Current research focuses on extending its capabilities beyond traditional image processing, including applications to graph data, partial differential equations, and high-dimensional point clouds, often employing novel architectures like convolutional neural operators and graph convolutional networks. These advancements improve model accuracy, efficiency (especially for edge devices), and robustness across diverse data types, impacting fields ranging from biomedical image analysis to remote sensing and anomaly detection.