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On Approximating the Dynamic Response of Synchronous Generators via Operator Learning: A Step Towards Building Deep Operator-based Power Grid Simulators.

, , , and . CoRR, (2023)

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A Physics-Guided Bi-Fidelity Fourier-Featured Operator Learning Framework for Predicting Time Evolution of Drag and Lift Coefficients., , , , , and . CoRR, (2023)Semantic analysis framework for protecting the power grid against monitoring-control attacks., , , and . IET Cyper-Phys. Syst.: Theory & Appl., 5 (1): 119-126 (2020)DeepONet-Grid-UQ: A Trustworthy Deep Operator Framework for Predicting the Power Grid's Post-Fault Trajectories., , , and . CoRR, (2022)DeepGraphONet: A Deep Graph Operator Network to Learn and Zero-shot Transfer the Dynamic Response of Networked Systems., , , and . CoRR, (2022)On Learning the Dynamical Response of Nonlinear Control Systems with Deep Operator Networks., , and . CoRR, (2022)Bayesian, Multifidelity Operator Learning for Complex Engineering Systems-A Position Paper., and . J. Comput. Inf. Sci. Eng., (December 2023)DAE-PINN: a physics-informed neural network model for simulating differential algebraic equations with application to power networks., and . Neural Comput. Appl., 35 (5): 3789-3804 (February 2023)DAE-PINN: A Physics-Informed Neural Network Model for Simulating Differential-Algebraic Equations with Application to Power Networks., and . CoRR, (2021)DeepGraphONet: A Deep Graph Operator Network to Learn and Zero-Shot Transfer the Dynamic Response of Networked Systems., , , and . IEEE Syst. J., 17 (3): 4360-4370 (September 2023)Semantic-Based Detection Architectures Against Monitoring-Control Attacks in Power Grids., , and . SmartGridComm, page 1-6. IEEE, (2019)