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Physics-Constrained neural network for solving discontinuous interface K-eigenvalue problem with application to reactor physics

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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 easy
to solve using traditional numerical methods albeit still challenging using neural networks for a wide range
of 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 lowest
eigenvalue 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 by
minimizing 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 from
the field of nuclear reactor physics. The methods were compared with the standard finite element method. The
applicability and accuracy of the methods are reported and indicate that PC-GIPMNN outperforms GIPMNN
and DRM.

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[V1] 2023-09-29 10:45:31 ChinaXiv:202310.00019V1 下载全文
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