https://doi.org/10.1051/epjconf/202429403002
Assimilating fission-code FIFRELIN using machine learning
1 CEA, DES, IRESNE, DER, SPRC, Cadarache, Physics Studies Laboratory, Saint-Paul-lès-Durance, 13108, France.
2 Université Paris-Saclay, CEA, Service de Génie Logiciel pour la Simulation, 91191, Gif-sur-Yvette, France.
* e-mail: guillaume.bazelaire@cea.fr
Published online: 17 April 2024
This paper presents work that has been done on the FIFRELIN Monte-Carlo code. The purpose of the code is to simulate the de-excitation process of fission fragments. Numerous quantity of insterest are calculated (mass yields, prompt particle spectra, mulitiplicities … ). Up to now the code relies on four free parameters which control the initial excitation and total angular momentum of fission fragment. Finding the good set of the free parameters is a diffucult task. In this work, we have developed an optimization algorithm based on Gaussian Process regression.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
