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DeepGraphONet: A Deep Graph Operator Network to Learn and Zero-shot Transfer the Dynamic Response of Networked Systems.

, , , and . CoRR, (2022)

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DeepGraphONet: A Deep Graph Operator Network to Learn and Zero-shot Transfer the Dynamic Response of Networked Systems., , , and . CoRR, (2022)DeepONet-Grid-UQ: A Trustworthy Deep Operator Framework for Predicting the Power Grid's Post-Fault Trajectories., , , and . CoRR, (2022)A Physics-Guided Bi-Fidelity Fourier-Featured Operator Learning Framework for Predicting Time Evolution of Drag and Lift Coefficients., , , , , and . CoRR, (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)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 . CoRR, (2021)Deep Operator Learning-based Surrogate Models with Uncertainty Quantification for Optimizing Internal Cooling Channel Rib Profiles., , , , and . CoRR, (2023)Developing correlation indices to identify coordinated cyber-attacks on power grids., and . IET Cyper-Phys. Syst.: Theory & Appl., 3 (4): 178-186 (2019)Learning the dynamical response of nonlinear non-autonomous dynamical systems with deep operator neural networks., , and . Eng. Appl. Artif. Intell., (2023)Semantic-Based Detection Architectures Against Monitoring-Control Attacks in Power Grids., , and . SmartGridComm, page 1-6. IEEE, (2019)