An efficient digital twin based on machine learning SVD autoencoder and generalised latent assimilation for nuclear reactor physics
Résumé
• A real-time operational digital twin is proposed for the prediction of power field in the core. • A non-intrusive forward model is built based on SVD-autoencoder and machine learning prediction methods. • An inverse model is realised based on a generalised latent assimilation method to overcome the bottleneck of efficient parameter identification.
Origine : Fichiers produits par l'(les) auteur(s)