基于物理增强分层神经网络的电力变换器深度学习建模

For the temporal modeling of power converters, recent approaches are primarily based on time-series data-driven approaches, suffering from high data quality and quantity requirements. Physics-informed neural networks represent a promising new formalism in data-driven modeling, but they still face high computational demands and generalization issues. Catering to these challenges, this article propo...