摘要: Machine learning-based modeling of reactor physics problems has attracted increasing interest in recent years.Despite some progress in one-dimensional problems, there is still a paucity of benchmark studies that are easyto solve using traditional numerical methods albeit still challenging using neural networks for a wide rangeof practical problems. We present two networks, namely the Generalized Inverse Power Method Neural Net#2;work (GIPMNN) and Physics-Constrained GIPMNN (PC-GIPIMNN) to solve K-eigenvalue problems in neu#2;tron diffusion theory. GIPMNN follows the main idea of the inverse power method and determines the lowesteigenvalue using an iterative method. The PC-GIPMNN additionally enforces conservative interface condi#2;tions for the neutron flux. Meanwhile, Deep Ritz Method (DRM) directly solves the smallest eigenvalue byminimizing the eigenvalue in Rayleigh quotient form. A comprehensive study was conducted using GIPMNN,PC-GIPMNN, and DRM to solve problems of complex spatial geometry with variant material domains fromthe field of nuclear reactor physics. The methods were compared with the standard finite element method. Theapplicability and accuracy of the methods are reported and indicate that PC-GIPMNN outperforms GIPMNNand DRM.