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              <p>Parmi les techniques dites d'Intelligence Artificielle Hybrides, les méthodes d'apprentissage informées par la physique ont suscité un intérêt croissant. Ces modèles fonctionnent principalement en imposant des biais de données, d'apprentissage ou d'architecture avec des données de simulation, des Équations aux Dérivées Partielles, ou des propriétés d'équivariance et d'invariance. Ces méthodes ont eu un grand succès dans des tâches impliquant un seul domaine physique, comme la mécanique des fluides, mais elles ne sont toujours pas adaptées aux tâches impliquant des phénomènes multi-physiques et multi-domaines complexes. De plus, elles sont principalement formulées comme des schémas d'apprentissage en un bloc (end-to-end), difficilement modulables. Pour relever ces défis, nous proposons de tirer parti des Bond Graphs, une approche de modélisation multi-physique, conjointement avec des Graph Neural Networks. Nous proposons un Neural Bond graph Encoder (NBgE) produisant des représentations informées par la multi-physique qui peuvent être intégrées dans n'importe quel modèle. Il offre une manière unifiée d'intégrer à la fois les biais de données et d'architecture dans l'apprentissage profond. Nos expériences sur deux systèmes physiques multi-domaines complexes - un Moteur à Courant Continu et le Système Respiratoire - démontrent l'efficacité de notre approche dans une tâche de prédiction de séries temporelles multivariées.</p>
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