The review concludes that hybrid PIML methods outperform data-driven models in accuracy, efficiency, and robustness for electricity system applications by embedding first-principles constraints.
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Engineering Hybrid Physics-Informed Neural Networks for Next-Generation Electricity Systems: A State-of-the-Art Review
The review concludes that hybrid PIML methods outperform data-driven models in accuracy, efficiency, and robustness for electricity system applications by embedding first-principles constraints.