Machine Learning Techniques for Data Reduction of Climate Applications

arXiv:2405.00879v1 Announce Type: new
Abstract: Scientists conduct large-scale simulations to compute derived quantities-of-interest (QoI) from primary data. Often, QoI are linked to specific features, regions, or time intervals, such that data can be adaptively reduced without compromising the integrity of QoI. For many spatiotemporal applications, these QoI are binary in nature and represent presence or absence of a physical phenomenon. We present a pipelined compression approach that first uses neural-network-based techniques to derive regions where QoI are highly likely to …

abstract applications arxiv binary climate compute cs.lg data data reduction features integrity machine machine learning machine learning techniques nature physics.ao-ph scale scientists simulations type