Data Reconstruction

Data reconstruction focuses on recovering missing or incomplete data from available information, a crucial task across diverse scientific and engineering domains. Current research emphasizes developing robust methods, particularly using deep learning architectures like diffusion models, autoencoders, and generative adversarial networks (GANs), to reconstruct data from various sources, including gradients in federated learning and incomplete sensor readings. These advancements address critical challenges in data privacy, anomaly detection, and efficient data transmission, impacting fields ranging from machine learning security to geophysical modeling.