Mapping Function

Mapping functions, which represent transformations between different data spaces, are a central focus in numerous scientific fields, aiming to learn these transformations effectively and efficiently. Current research emphasizes learning operator-to-function mappings using neural networks, particularly DeepONets, and adapting these techniques for diverse applications like solving partial differential equations and medical imaging. These advancements are improving the accuracy and efficiency of various tasks, ranging from robotic control and 3D mapping to program analysis and fair machine learning. The development of robust and generalizable mapping functions holds significant potential for advancing numerous scientific disciplines and practical applications.