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arXiv Machine Learning By Stefano Riva, Carolina Introini, J. Nathan Kutz, Antonio Cammi

Towards Efficient Parametric State Estimation in Circulating Fuel Reactors with Shallow Recurrent Decoder Networks

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This paper presents a novel Shallow Recurrent Decoder network for efficient parametric state estimation in circulating fuel reactors, specifically applied to the Molten Salt Fast Reactor (MSFR). The model infers the full reactor state—including neutron fluxes, precursor concentrations, temperature, pressure, and velocity—using only three out‑of‑core neutron flux time‑series measurements, while also handling parametric time‑series data to explore different accident scenarios. The approach demonstrates accurate real‑time reconstruction with low training cost and provides uncertainty quantification, making it suitable for monitoring and control within a reactor digital twin.

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