A physics-informed latent neural ODE, which blends a nominal droop-control inverter model with learned neural dynamics, reproduces proprietary grid-forming inverter behavior better than an RNN in a load-step test.
Physics-informed neural networks: A deep learn- ing framework for solving forward and inverse problems involving nonlinear partial differential equations,
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Physics-Informed Learning of Proprietary Inverter Models for Grid Dynamic Studies
A physics-informed latent neural ODE, which blends a nominal droop-control inverter model with learned neural dynamics, reproduces proprietary grid-forming inverter behavior better than an RNN in a load-step test.