איתור ואסטימציה תקינה באמצעות קודרים רציים עמוקים ומוגבלים במערכת TRIGA Mark II

תקציר מקורי באנגליתarXiv:2510.12368v2 Announce Type: replace-cross Abstract: Shallow Recurrent Decoder networks are a novel data-driven methodology able to provide accurate state estimation in engineering systems, such as nuclear reactors. This deep learning architecture is a robust technique designed to map the temporal trajectories of a few sparse measures to the full state space, including unobservable fields, which is agnostic to sensor positions and able to handle noisy data through an ensemble strategy, leveraging the short training times and without the need for hyperparameter tuning. The architecture was successfully applied to the Molten Salt Fast Reactor concept; now, this work considers the performance of Shallow Recurrent Decoders on a deployed reactor concept. The underlying model is represented