  This letter devises an AI-Inverter that pilots the use of a physics-informed
neural network (PINN) to enable AI-based electromagnetic transient simulations
(EMT) of grid-forming inverters. The contributions are threefold: (1) A
PINN-enabled AI-Inverter is formulated; (2) An enhanced learning strategy,
balanced-adaptive PINN, is devised; (3) extensive validations and comparative
analysis of the accuracy and efficiency of AI-Inverter are made to show its
superiority over the classical electromagnetic transient programs (EMTP).
