用生成模型提升核反应堆安全预测精度与可靠性
Predicting Critical Heat Flux with Uncertainty Quantification and Domain Generalization Using Conditional Variational Autoencoders and Deep Neural Networks
- 基于条件变分自编码器生成稀缺的临界热流密度数据
- 生成数据使模型在训练内外域误差均低于5%，且不确定性更稳定
- 适合从事核能安全、工业仿真与不确定性建模的研究者
深度生成模型可生成接近原始数据分布的合成样本，缓解数据稀缺问题。本文构建了条件变分自编码器（CVAE）以扩充用于2006年Groeneveld查表法的临界热流密度（CHF）数据。为对比传统方法，使用微调的深度神经网络（DNN）回归模型在同一数据集上进行评估。两者均实现小的平均绝对相对误差，其中CVAE表现更优。通过重复采样和集成学习进行不确定性量化（UQ），DNN集成提升了性能，但CVAE在训练内外域保持更一致的结果，方差更小，置信度更高。在训练域内外误差均较小，仅在外域略大。总体而言，CVAE在预测准确性和不确定性行为上优于DNN。
原文摘要 · Abstract (English)
Deep generative models (DGMs) can generate synthetic data samples that closely resemble the original dataset, addressing data scarcity. In this work, we developed a conditional variational autoencoder (CVAE) to augment critical heat flux (CHF) data used for the 2006 Groeneveld lookup table. To compare with traditional methods, a fine-tuned deep neural network (DNN) regression model was evaluated on the same dataset. Both models achieved small mean absolute relative errors, with the CVAE showing more favorable results. Uncertainty quantification (UQ) was performed using repeated CVAE sampling and DNN ensembling. The DNN ensemble improved performance over the baseline, while the CVAE maintained consistent results with less variability and higher confidence. Both models achieved small errors inside and outside the training domain, with slightly larger errors outside. Overall, the CVAE performed better than the DNN in predicting CHF and exhibited better uncertainty behavior.
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