FEW-GROUP CROSS SECTIONS LIBRARY BY ACTIVE LEARNING WITH SPLINE KERNELS

Abstract

This work deals with the representation of homogenized few-groups cross sections libraries by machine learning. A Reproducing Kernel Hilbert Space (RKHS) is used for different Pool Active Learning strategies to obtain an optimal support. Specifically a spline kernel is used and results are compared to multi-linear interpolation as used in industry, discussing the reduction of the library size and of the overall performance. A standard PWR fuel assembly provides the use case (OECD-NEA Burn-up Credit Criticality Benchmark [1]).

Keywords

• homogenized cross sections
• machine learning
• kernel methods
• pool active learning