论文标题
基于自动编码器的缩小阶模型，用于将其应用于中子扩散的特征值问题
An autoencoder-based reduced-order model for eigenvalue problems with application to neutron diffusion
论文作者
论文摘要
本文使用自动编码器降低维度，为特征值问题提供了一种新颖的基于投影的减少阶模型。降低的建模依赖于找到合适的基础函数，该功能定义了一个低维空间，在该空间中，高维系统被近似。适当的正交分解（POD）和单数值分解（SVD）通常用于此目的，并产生最佳的线性子空间。自动编码器提供了POD/SVD的非线性替代方案，可以更有效地捕获高保真模型结果中的特征或模式。 开发了基于自动编码器和新型混合SVD-AutoEncoder的减少阶模型。将这些方法与标准POD-Galerkin方法进行比较，并应用于从核反应堆物理领域采取的两个测试用例。
Using an autoencoder for dimensionality reduction, this paper presents a novel projection-based reduced-order model for eigenvalue problems. Reduced-order modelling relies on finding suitable basis functions which define a low-dimensional space in which a high-dimensional system is approximated. Proper orthogonal decomposition (POD) and singular value decomposition (SVD) are often used for this purpose and yield an optimal linear subspace. Autoencoders provide a nonlinear alternative to POD/SVD, that may capture, more efficiently, features or patterns in the high-fidelity model results. Reduced-order models based on an autoencoder and a novel hybrid SVD-autoencoder are developed. These methods are compared with the standard POD-Galerkin approach and are applied to two test cases taken from the field of nuclear reactor physics.
