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ML/AI methods have been introduced in nuclear reactor applications during the last decades; they contribute today to enhance the safety of the current fleet of reactors. however, these statistic techniques are becoming increasingly more practical and powerful in recent years. Hence, these current developments foretell an increase of applying ML/AI-based methods to solving issues pertaining to nuclear science and technology. |
Schematic figure of the analysis pathway that informs nuclear physics with differential and integral information Professor Óscar Cabellos is expert in ML/AI applications in nuclear science and engineering in the Instituto de Fusión Nuclear Guillermo Velarde. He works with ML techniques to study nuclear data integrating differential and integral experiments. The combination of information from differential experimental data, nuclear-physics theory, integral experiments, and neutron-transport simulations of the latter experiments with the help of the random forest algorithm and expert judgment allows to improve the description of nuclear-physics observables as applied to a particular application area.