Machine Learning Methods for Scientific Data Compression

This groundbreaking book, Machine Learning Methods for Scientific Data Compression, delivers an essential exploration into the rapidly evolving field of data reduction for scientific applications. As scientific simulations generate petabytes of data, traditional compression methods falter in maintaining critical fidelity. This work introduces novel machine learning approaches, from advanced autoencoders to generative foundation models, all designed to achieve unprecedented compression ratio...