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 be present. Then, we employ a Guaranteed Autoencoder (GAE) to
compress data with differential error bounds. GAE uses QoI information to apply
low-error compression to only these regions. This results in overall high
compression ratios while still achieving downstream goals of simulation or data
collections. Experimental results are presented for climate data generated from
the E3SM Simulation model for downstream quantities such as tropical cyclone
and atmospheric river detection and tracking. These results show that our
approach is superior to comparable methods in the literature.

科学家们提出了一种流水线压缩方法，利用神经网络和保证自编码器进行数据压缩，以实现高压缩比同时达到模拟或数据收集的目标，并且实验证明该方法优于文献中的可比方法。