Reconstruction of few-group homogenized cross section by kernel method and active learning

Abstract

International audienceThis work deals with the approximation of homogenized few-groups cross sections by kernel methods. Different kernels types are used in conjunction with pool active learning to optimize the cross section's support. They are compared to multi-variate splines and multi-linear interpolation, similar to industry. A standard PWR fuel assembly is provided by the OECD-NEA Burn-up Credit Criticality Benchmark, Phase-IID, to evaluate their performances