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.

翻译：现代科学模拟、观测及大规模实验产生的数据量常超出存储、处理与分析能力的极限。这一挑战推动了数据缩减方法的发展，旨在高效管理海量数据集，同时保留关键的物理特征与目标物理量。在许多科学工作流中，从压缩表示中恢复数据（即超分辨率任务）同样至关重要，且需确保关键物理特性的保留。一个典型应用是检查点与重启机制，这对长期运行的模拟在故障恢复、中断后继续执行或检查中间结果时不可或缺。本文提出一种基于指数族学习最新进展的科学数据压缩与超分辨率新框架。该方法能保留并量化目标物理量的不确定性，并支持在压缩比与重建保真度之间进行灵活权衡。