用扩散采样实现工业数据模型的内在校准不确定性量化。
Towards Intrinsically Calibrated Uncertainty Quantification in Industrial Data-Driven Models via Diffusion Sampler
- 基于扩散模型进行后验采样，天然生成校准的不确定性。
- 在多个真实工业场景中，不确定性校准与预测精度均优于现有方法。
- 适合需要高可靠性的工业过程监控与决策系统使用。
在现代流程工业中，数据驱动模型是关键性能指标难以直接测量时实现实时监控的重要工具。虽然准确预测至关重要，但可靠的不确定性量化（UQ）对于安全、可靠性和决策同样关键，但仍是当前数据驱动方法的主要挑战。本文提出一种基于扩散的后验采样框架，通过忠实的后验采样实现内在校准的预测不确定性，无需事后校准。在合成分布、基于拉曼光谱的苯乙酸软传感器基准以及真实的氨合成案例研究中，该方法在不确定性校准和预测准确性方面均取得实际提升。结果表明，扩散采样器为工业应用中的不确定性感知建模提供了一种原则性强且可扩展的新范式。
原文摘要 · Abstract (English)
In modern process industries, data-driven models are important tools for real-time monitoring when key performance indicators are difficult to measure directly. While accurate predictions are essential, reliable uncertainty quantification (UQ) is equally critical for safety, reliability, and decision-making, but remains a major challenge in current data-driven approaches. In this work, we introduce a diffusion-based posterior sampling framework that inherently produces well-calibrated predictive uncertainty via faithful posterior sampling, eliminating the need for post-hoc calibration. In extensive evaluations on synthetic distributions, the Raman-based phenylacetic acid soft sensor benchmark, and a real ammonia synthesis case study, our method achieves practical improvements over existing UQ techniques in both uncertainty calibration and predictive accuracy. These results highlight diffusion samplers as a principled and scalable paradigm for advancing uncertainty-aware modeling in industrial applications.
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