论文标题
深层蒙特卡洛分位回归，用于量化物理温度场重建中的息肉不确定性
Deep Monte Carlo Quantile Regression for Quantifying Aleatoric Uncertainty in Physics-informed Temperature Field Reconstruction
论文作者
论文摘要
对于温度场重建（TFR），由于卷积层的良好图像特征提取能力，卷积神经网络（CNN）是一个强大的替代模型。但是，训练CNN需要大量标记的数据，并且常见的CNN无法量化数据噪声引起的不确定性。在实际的工程中，几乎无法获得TFR的无噪声和标记的培训数据。为了解决这两个问题，本文提出了一种深层蒙特卡洛分位回归（深MC-QR）方法，用于重建温度场并量化数据噪声引起的不确定性。一方面，深度MC-QR方法使用物理知识来指导CNN的训练。因此，深度MC-QR方法可以在没有任何标记培训数据的情况下重建准确的TFR替代模型。另一方面，深MC-QR方法为每个训练时期的每个输入构建了一个分位级图像。然后，训练有素的CNN模型可以通过在预测阶段中通过分位数级别图像采样来量化差异不确定性。最后，通过许多实验验证了所提出的深MC-QR方法的有效性，并分析了数据噪声对TFR的影响。
For the temperature field reconstruction (TFR), a complex image-to-image regression problem, the convolutional neural network (CNN) is a powerful surrogate model due to the convolutional layer's good image feature extraction ability. However, a lot of labeled data is needed to train CNN, and the common CNN can not quantify the aleatoric uncertainty caused by data noise. In actual engineering, the noiseless and labeled training data is hardly obtained for the TFR. To solve these two problems, this paper proposes a deep Monte Carlo quantile regression (Deep MC-QR) method for reconstructing the temperature field and quantifying aleatoric uncertainty caused by data noise. On the one hand, the Deep MC-QR method uses physical knowledge to guide the training of CNN. Thereby, the Deep MC-QR method can reconstruct an accurate TFR surrogate model without any labeled training data. On the other hand, the Deep MC-QR method constructs a quantile level image for each input in each training epoch. Then, the trained CNN model can quantify aleatoric uncertainty by quantile level image sampling during the prediction stage. Finally, the effectiveness of the proposed Deep MC-QR method is validated by many experiments, and the influence of data noise on TFR is analyzed.
