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DOE OSTI · 3028380

Application of Machine Learning to Multigroup Microscopic Cross Sections

Calvin, Olin W [Idaho National Laboratory] (ORCID:0000000195600356)·Wang, Yaqi [Idaho National Laboratory] (ORCID:0000000237373498)·Ortensi, Javier [Idaho National Laboratory] (ORCID:0000000316853916)·Dhulipala, Som LakshmiNarasimha [Idaho National Laboratory] (ORCID:0000000208014250)·Che, Yifeng [Georgia Institute of Technology] (ORCID:0000000318419937)·Balestra, Paolo [X-energy] (ORCID:0000000219837201)
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Abstract

Presentation discussing the research and development of deep neural network models for modeling microscopic neutron cross-section data in the Griffin reactor physics application for pebble-bed reactors. This work details advancements made between the last review meeting in July 2024 until July 2025.

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22 - GENERAL STUDIES OF NUCLEAR REACTORS97 - MATHEMATICS AND COMPUTINGartificial intelligencedeep neural networkGriffinmachine learningneutron cross section

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Calvin, Olin W [Idaho National Laboratory] (ORCID:0000000195600356), Wang, Yaqi [Idaho National Laboratory] (ORCID:0000000237373498), Ortensi, Javier [Idaho National Laboratory] (ORCID:0000000316853916), Dhulipala, Som LakshmiNarasimha [Idaho National Laboratory] (ORCID:0000000208014250), Che, Yifeng [Georgia Institute of Technology] (ORCID:0000000318419937), Balestra, Paolo [X-energy] (ORCID:0000000219837201). 2025-07-30. Application of Machine Learning to Multigroup Microscopic Cross Sections. https://www.osti.gov/biblio/3028380

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