Synthetic Coordinate

Synthetic coordinates represent a burgeoning area of research focused on creating artificial coordinate systems for various data types, primarily to overcome limitations of real-world data acquisition or to improve model performance. Current research emphasizes the development of algorithms, such as generative adversarial networks (GANs) and equivariant graph neural networks (EGNNs), to generate these synthetic coordinates for applications ranging from molecular graph generation and geometric reasoning to spatial data privacy preservation. This approach offers significant advantages by enabling the use of advanced models that require coordinate information even when such data is unavailable or expensive to obtain, leading to improved accuracy and efficiency in diverse scientific and engineering domains.