TOPIQ：有损压缩下关注量预测的统计误差传播
TOPIQ: Statistical Error Propagation for Quantity-of-Interest Prediction under Lossy Compression
AI总结：
针对有损压缩下逐元素误差边界无法转化为下游关注量边界的问题，提出TOPIQ框架，通过分解关注量为基础算子的统计误差传播方法，实现高效且校准良好的关注量偏差与不确定性预测，可集成至AI分析流水线。
AI中文摘要：
有损压缩对于管理海量科学数据至关重要，但逐元素误差边界无法转化为下游关注量（QoI，如区域平均值、神经网络预测或多场导出量）的边界。我们提出TOPIQ，一种统计误差传播框架，可从紧凑压缩元数据（不足原始数据的0.1%）中预测QoI级别的偏差与不确定性。TOPIQ将QoI分解为具有闭式传播规则的基础算子，规则考虑空间误差相关性与数据-误差耦合；新QoI可在运行时通过组合支持，无需针对每个QoI单独推导或重新训练。在覆盖4个数据集、3种压缩器、4类QoI及8种误差边界的552次评估中，93.1%的配置实现了校准良好的预测。预计算的元数据支持对任意查询区域进行事后不确定性量化，速度较直接计算提升56倍至402倍。案例研究展示了其与AI驱动分析流水线的集成，可为动态组合的查询提供端到端置信区间。
英文摘要：
Lossy compression is essential for managing massive scientific data, but per-element error bounds do not translate into bounds on downstream quantities of interest (QoIs) such as regional averages, neural network predictions, or multi-field derived quantities. We present TOPIQ, a statistical error-propagation framework that predicts QoI-level bias and uncertainty from compact compression metadata (less than 0.1% of original data). TOPIQ decomposes QoIs into primitive operators with closed-form propagation rules accounting for spatial error correlation and data-error coupling; new QoIs are supported by composition at runtime with no per-QoI derivation or retraining. Across 552 evaluations spanning 4 datasets, 3 compressors, 4 QoI families, and 8 error bounds, 93.1% of configurations achieve well-calibrated predictions. Pre-computed metadata enables post-hoc uncertainty quantification for arbitrary query regions at 56x-402x speedup over direct computation. A case study demonstrates integration into an AI-driven analysis pipeline with end-to-end confidence intervals for dynamically composed queries.
