Scientific Data Compression and Super-Resolution Sampling

TL;DR

This paper introduces a novel framework for scientific data compression and super-resolution that leverages learning exponential families to efficiently manage large datasets while preserving physical features and quantifying uncertainty.

Contribution

It presents a new method that combines data compression and super-resolution with uncertainty quantification, tailored for scientific data workflows.

Findings

Supports flexible trade-offs between compression ratio and fidelity

Preserves and quantifies uncertainty in physical quantities

Enables efficient data recovery and analysis

Abstract

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.