论文标题
使用机器学习的确定性中子传输计算中的数据减少
Data Reduction in Deterministic Neutron Transport Calculations Using Machine Learning
论文作者
论文摘要
对于每种材料，温度和富集水平，需要用于裂变和散射数据的中子横截面矩阵才能准确计算中子传输方程。当能量组的数量较大时，使用多组离散尺寸（SN）方法时，此信息可能是一个限制因素。机器学习（ML）可用于通过将标量通量映射到散射和裂变源的函数来替代横截面矩阵的需求。通过使用自动编码器和深度信息的神经网络（DJINN），对于618组问题，数据存储要求减少了94％的原始数据。这是在保留标量通量，保持通用性并减少壁时钟时间的同时完成的。
Neutron cross section matrices for fission and scattering data are required for each material, temperature, and enrichment level to calculate the neutron transport equation accurately. This information can be a limiting factor when using the multigroup discrete ordinates (SN) method when the number of energy groups is large. Machine Learning (ML) can be used to replace the need for the cross section matrices by reproducing the function that maps the scalar flux to the scattering and fission sources. Through the use of autoencoders and Deep Jointly-Informed Neural Networks (DJINN), the data storage requirements are reduced by 94% of the original data for a 618 group problem. This is accomplished while preserving the scalar flux, maintaining generality, and decreasing wall clock times.
