提出一种可量化不确定性的科学数据压缩与超分辨率恢复框架。
Scientific Data Compression and Super-Resolution Sampling
- 基于学习指数族的新型压缩方法，支持灵活压缩比与重建精度权衡。
- 能有效保留关键物理量的不确定性，确保恢复结果可信。
- 适用于长时间模拟的断点续传与中间结果分析。
现代科学模拟、观测和大规模实验产生的数据量常超出存储、处理和分析的极限。这一挑战推动了高效数据压缩方法的发展，以在保留关键物理特征和关注量的前提下管理海量数据。在许多科学工作流中，从压缩表示中可靠恢复数据（即超分辨率）至关重要，且需保证关键物理特性的保真度。典型应用如断点续传，对长时模拟的故障恢复、中断后重启或中间结果检查极为重要。本文提出一种基于学习指数族的科学数据压缩与超分辨率新框架，能够精确刻画并量化物理量的不确定性，支持压缩率与重建质量之间的灵活权衡。
原文摘要 · Abstract (English)
Modern scientific simulations, observations, and large-scale experiments generate data at volumes that often exceed the limits of storage, processing, and analysis. This challenge drives the development of data reduction methods that efficiently manage massive datasets while preserving essential physical features and quantities of interest. In many scientific workflows, it is also crucial to enable data recovery from compressed representations - a task known as super-resolution - with guarantees on the preservation of key physical characteristics. A notable example is checkpointing and restarting, which is essential for long-running simulations to recover from failures, resume after interruptions, or examine intermediate results. In this work, we introduce a novel framework for scientific data compression and super-resolution, grounded in recent advances in learning exponential families. Our method preserves and quantifies uncertainty in physical quantities of interest and supports flexible trade-offs between compression ratio and reconstruction fidelity.
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