<ul><li>Universal Differential Equations (UDEs) combine neural networks with physical differential equations for scientific machine learning, aiding data-efficient and interpretable modeling.</li><li>In the smart grid domain, modeling node-wise battery dynamics poses challenges due to varying solar input and household load profiles, leading to the proposal of a UDE-based approach.</li><li>This approach utilizes synthetic data to simulate battery dynamics, with a neural residual capturing unmodeled dynamics arising from diverse node demand and environmental conditions.</li><li>Experiments show that the UDE model closely matches actual battery trajectories, demonstrates smooth convergence, and remains stable in long-term forecasts, indicating its effectiveness for battery modeling in renewable-integrated smart grids.</li></ul>

Universal Differential Equations for Scientific Machine Learning of Node-Wise Battery Dynamics in Smart Grids

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