Physics-Informed Neural Networks in Grid-Connected Inverters: A Review

Physics-Informed Neural Networks (PINNs) have emerged as a powerful tool for modeling and controlling complex energy systems by embedding physical laws into deep learning architectures. This review paper highlights the application of PINNs in grid-connected inverter systems (GCISs), categorizing the...

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Bibliographic Details

Published in:	Energies
Main Authors:	Ekram Al Mahdouri, Said Al-Abri, Hassan Yousef, Ibrahim Al-Naimi, Hussein Obeid
Format:	Article
Language:	English
Published:	MDPI AG 2025-10-01
Subjects:	physics-informed neural networks data-driven modeling grid-connected inverters parameter estimation state estimation control strategies
Online Access:	https://www.mdpi.com/1996-1073/18/20/5441