Towards Intrinsically Calibrated Uncertainty Quantification in Industrial Data-Driven Models via Diffusion Sampler

2026-04-02

AI Summary

Industrial data-driven models often lack reliable and intrinsically calibrated uncertainty quantification, limiting their deployment in safety-critical applications. This work proposes a diffusion sampling–based posterior inference framework that, for the first time, integrates diffusion probabilistic models into industrial uncertainty quantification. By directly sampling from the posterior predictive distribution, the method yields well-calibrated predictions without requiring post-hoc calibration. It achieves intrinsic calibration while maintaining high predictive accuracy, consistently outperforming existing approaches across synthetic benchmarks, Raman spectroscopy soft sensors, and an industrial ammonia synthesis case study, with simultaneous improvements in both calibration quality and prediction accuracy.

Abstract

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.

Problem

• uncertainty quantification
• data-driven models
• calibration
• industrial applications
• predictive uncertainty

Innovation

• diffusion sampler
• uncertainty quantification
• posterior sampling
• intrinsic calibration
• industrial data-driven models