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: 104
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: 2023
: .., .. - : // . 104. .: , 2023. .118-134. DOI: https://doi.org/10.25728/ubs.2023.104.5
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(.): data assimilation, machine learning, uncertainty analysis, neutronics modelling, nuclear facilities, reactor experiments
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(.): The article presents an analysis of the current state and prospects for the development of data assimilation procedures in neutronics modelling. These procedures are used to refine the parameters of neutronics models based on reactor experiment results, improve the accuracy of calculations predicting the characteristics of nuclear facilities under development, and plan informative experiments that closely match the neutronics properties of the facilities being designed. The article provides a classification of the approaches used in data assimilation for neutronics modelling, discusses the areas of application, advantages, and disadvantages of various implementations, identifies the main trends and directions for further development, and considers the current scientific and applied problems in the subject area.
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