Detection and localisation of multiple in-core perturbations with neutron noise-based self-supervised domain adaptation

Problem Case
• We aim to unfold reactor transfer function to provide core diagnostics.
• Derivation of core perturbation characteristics to classify and locate its origin.
• Yet this is challenging due to the limited number of neutron detectors in
western type reactors.
• We ask, can we use machine learning to successfully approximate the reactor
transfer function?
• However, to effectively train ML algorithms large quantities of data are
required.

More details can be found in the presentation file