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

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 and results are compared multi-linear interpolation as in industry, discussing reduction library size overall performance. standard PWR fuel assembly provides use case (OECD-NEA Burn-up Credit Criticality Benchmark [1]).