提出三种深度学习流场测量不确定性量化方法，验证了其中最优方案。
On Uncertainty Prediction for Deep-Learning-based Particle Image Velocimetry
- 用神经网络、多模型和多变换三种方式估计预测不确定性
- 不确定性网络（UNN）在多种数据集上表现最佳，误差估算更准
- 适合需要可靠置信度的科研与工程流场测量应用
粒子图像测速（PIV）是一种广泛用于流场测量的技术，传统上依赖互相关算法追踪粒子位移。近年来，基于深度学习的方法显著提升了PIV的精度与效率。然而，尽管其重要性突出，深度学习方法在PIV中的可靠不确定性量化仍是一个关键且被忽视的挑战。本文探讨了三种不确定性量化方法：不确定性神经网络（UNN）、多模型（MM）和多变换（MT），并在多个数据集上进行评估。结果表明，三种方法在轻微扰动下均表现良好。在三个评价指标中，UNN方法始终表现最优，提供准确的不确定性估计，展现出在深度学习基PIV中进行不确定性量化的重要潜力。本研究为PIV中的不确定性量化提供了综合性框架，为未来研究与实际应用提供参考。
原文摘要 · Abstract (English)
Particle Image Velocimetry (PIV) is a widely used technique for flow measurement that traditionally relies on cross-correlation to track the displacement. Recent advances in deep learning-based methods have significantly improved the accuracy and efficiency of PIV measurements. However, despite its importance, reliable uncertainty quantification for deep learning-based PIV remains a critical and largely overlooked challenge. This paper explores three methods for quantifying uncertainty in deep learning-based PIV: the Uncertainty neural network (UNN), Multiple models (MM), and Multiple transforms (MT). We evaluate the three methods across multiple datasets. The results show that all three methods perform well under mild perturbations. Among the three evaluation metrics, the UNN method consistently achieves the best performance, providing accurate uncertainty estimates and demonstrating strong potential for uncertainty quantification in deep learning-based PIV. This study provides a comprehensive framework for uncertainty quantification in PIV, offering insights for future research and practical implementation.
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