Reduce SCATTER

Reducing scatter, the variability around a central trend in data, is a crucial objective across diverse scientific fields, aiming to improve the accuracy and reliability of models and predictions. Current research focuses on developing novel algorithms and architectures, such as sparse communication frameworks (e.g., SparDL) and physics-informed neural networks (PINNs) with region optimization, to minimize scatter in various contexts, including photonic computing, image analysis, and astrophysical scaling relations. These advancements enhance the precision of estimations in fields ranging from AI acceleration and 3D object detection to cosmological parameter inference, ultimately leading to more robust and reliable scientific findings and technological applications.