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

Title: Reconstruction of few-group homogenized cross section by kernel method and active learning
Authors: Szames, Ea.; Tomatis, D.; Ammar, K.; Martinez, Jm.
Contributors: CEA-Direction des Energies (ex-Direction de l'Energie Nucléaire) (CEA-DES (ex-DEN)); Commissariat à l'énergie atomique et aux énergies alternatives (CEA)
Source: M and C 2019 ; https://hal.science/hal-02411076 ; M and C 2019, Aug 2019, Portland, United States
Publisher Information: CCSD
Publication Year: 2019
Collection: HAL-CEA (Commissariat à l'énergie atomique et aux énergies alternatives)
Subject Terms: Few-Group Cross Sections; Kernel Methods; Active Learning; Spline Interpolation; [PHYS.NEXP]Physics [physics]/Nuclear Experiment [nucl-ex]; [PHYS.NUCL]Physics [physics]/Nuclear Theory [nucl-th]
Subject Geographic: Portland; United States
Description: International audience ; This 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 .
Document Type: conference object
Language: English
Availability: https://hal.science/hal-02411076; https://hal.science/hal-02411076v1/document; https://hal.science/hal-02411076v1/file/201800004324.pdf
Rights: info:eu-repo/semantics/OpenAccess
Accession Number: edsbas.DDB2BFAC
Database: BASE