Error Bounded Lossy

Error-bounded lossy compression aims to significantly reduce the size of large scientific datasets while guaranteeing a maximum acceptable level of data distortion. Current research focuses on developing novel algorithms, often incorporating neural networks (like autoencoders and super-resolution networks) or multigrid methods, to achieve high compression ratios with precise error control tailored to specific data types (e.g., scientific simulations, pre-trained machine learning models). This is crucial for addressing the storage and I/O bottlenecks in various fields, including climate modeling, deep learning, and federated learning, enabling faster processing and analysis of massive datasets. The resulting improvements in efficiency and scalability have significant implications for scientific discovery and practical applications.