Engineering Hybrid Physics-Informed Neural Networks for Next-Generation Electricity Systems: A State-of-the-Art Review

2026-05-22 · Source: cs.NE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation · Depth: Advanced, quick

Summary

A state-of-the-art review examines hybrid Physics-Informed Machine Learning (PIML) architectures, including PINNs, DeepONets, Fourier Neural Operators, Extreme Learning Machine-enhanced PINNs, graph-based PIGNNs, and domain-decomposition PINNs, for next-generation electricity systems. This approach integrates domain-specific physics into machine learning to overcome data scarcity, interpretability issues, and the need to enforce physical laws inherent in purely data-driven models. The review covers case studies in field analysis, fault detection, digital twins, surrogate modeling, and control optimization. Findings indicate that embedding governing equations like Maxwell's significantly improves predictive accuracy with sparse and noisy data, reduces simulation time by orders of magnitude compared to finite element methods, and enhances generalization across operating regimes. Hybrid PIML frameworks consistently outperform purely data-driven baselines in parameter sensitivity, dynamic behavior, and robustness, while supporting real-time digital-twin calibration and uncertainty quantification. Challenges include training instability for stiff multi-scale problems and computational costs.

Key takeaway

For Machine Learning Engineers developing models for next-generation electricity systems, you should prioritize integrating physics-informed machine learning (PIML) approaches. Purely data-driven models struggle with data scarcity and interpretability, whereas PIML, by embedding governing equations, significantly improves predictive accuracy and generalization. This enables more robust fault detection, efficient digital twins, and optimized control, shifting your strategy from black-box methods to transparent, physics-informed solutions for resilient infrastructure.

Key insights

Physics-informed machine learning integrates domain physics into models, enhancing accuracy and efficiency for data-scarce electricity systems.

Principles

• Embedding physical laws improves predictive accuracy.
• Hybrid PIML outperforms data-driven baselines.
• PIML reduces simulation time orders of magnitude.

Method

Physics-informed machine learning embeds governing equations, like Maxwell's, directly into neural network training to enforce physical laws and improve model performance.

In practice

• Implement PIML for fault detection.
• Develop PIML-driven digital twins.
• Optimize electricity system control.

Topics

• Physics-Informed Neural Networks
• Electricity Systems
• Digital Twins
• Fault Detection
• Surrogate Modeling
• DeepONets

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.NE updates on arXiv.org.