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Physics-Embedded Graph Learning Unlocks Integrated Energy System Modeling
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DOI：10.1016/j.egyai.2025.100597.png)
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Abstract
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• Proposing AI-based approaches for energy system modeling from a graph perspective. • Modeling spatiotemporal dynamics through state transitions and topology. • Embedding physically governed mechanisms into the neural network design. • Exploring neural networks' physical interpretability from fluid dynamics.
Keywords:
Integrated energy system
Interpretable AI
Dynamic characteristic
Fluid mechanics
Graph neural network
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