Abstract In this paper, the deep learning based on the artificial neural network (ANN), which is referred to as the deep neural network (DNN), is adopted to build a new model for the generation of the resonance self-shielded cross-sections (XSs). In this model, using the dataset generated from the pin-based ultra-fine-group (UFG) calculations under a multi-dimensional parameter table, the multi-layer DNN is trained to learn the underlying relationship between resonance self-shielded XSs and correlated parameters. Then the trained DNN is used for further practical calculations, which takes a negligible computing time. The computing accuracy of this model is tested through the generated datasets and practical PWR problems, and numerical results show that the new model is a promising approach for the generation of the resonance self-shielded XSs.

中文翻译：

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深度神经网络在共振自屏蔽截面生成中的应用

摘要 本文采用基于人工神经网络（ANN）的深度学习，简称深度神经网络（DNN），建立了共振自屏蔽截面的生成新模型。 （XS）。在该模型中，使用多维参数表下基于引脚的超细群 (UFG) 计算生成的数据集，训练多层 DNN 以了解共振自屏蔽 XS 与相关参数。然后将训练好的 DNN 用于进一步的实际计算，所需的计算时间可以忽略不计。通过生成的数据集和实际压水堆问题对该模型的计算精度进行了测试，数值结果表明新模型是一种很有前途的谐振自屏蔽 XS 生成方法。