论文标题
NET-PRO：裂纹模式具有量化不确定性的神经过程
Peri-Net-Pro: The neural processes with quantified uncertainty for crack patterns
论文作者
论文摘要
本文使用peridenanic理论，非常适合裂纹研究，以预测移动磁盘中的裂纹模式并根据模式对它们进行分类，并最终执行回归分析。这样，使用Peridynemics通过分子动力学（MD）模拟根据每种模式获得裂纹模式。图像分类和回归研究是通过卷积神经网络（CNN）和神经过程进行的。首先，我们使用Peridyanics提高了数据的数量和质量，从理论上讲，这可以补偿有限元方法（FEM）生成裂纹模式图像中的问题。其次，我们为使用Peridynanic理论获得的PMB，LPS和VES模型进行了案例研究。进行了案例研究以使用CNN对图像进行分类，并确定PMB，LB和VES模型的适用性。最后，我们对具有神经过程的裂纹模式图像进行了回归分析，以预测裂纹模式。在回归问题中，通过代表时期的方差结果，可以证实，通过通过神经过程增加时期数来降低方差的结果。这项研究的最关键点是，即使缺少或不足的培训数据，神经过程也可以进行准确的预测。
This paper uses the peridynamic theory, which is well-suited to crack studies, to predict the crack patterns in a moving disk and classify them according to the modes and finally perform regression analysis. In that way, the crack patterns are obtained according to each mode by Molecular Dynamic (MD) simulation using the peridynamics. Image classification and regression studies are conducted through Convolutional Neural Networks (CNNs) and the neural processes. First, we increased the amount and quality of the data using peridynamics, which can theoretically compensate for the problems of the finite element method (FEM) in generating crack pattern images. Second, we did the case study for the PMB, LPS, and VES models that were obtained using the peridynamic theory. Case studies were performed to classify the images using CNNs and determine the PMB, LBS, and VES models' suitability. Finally, we performed the regression analysis for the images of the crack patterns with neural processes to predict the crack patterns. In the regression problem, by representing the results of the variance according to the epochs, it can be confirmed that the result of the variance is decreased by increasing the epoch numbers through the neural processes. The most critical point of this study is that the neural processes make an accurate prediction even if there are missing or insufficient training data.
