| B-DeepONet: An enhanced Bayesian DeepONet for solving noisy parametric PDEs using accelerated replica exchange SGLD G Lin, C Moya, Z Zhang Journal of Computational Physics 473, 111713, 2023 | 52* | 2023 |
| Deeponet-grid-uq: A trustworthy deep operator framework for predicting the power grid’s post-fault trajectories C Moya, S Zhang, G Lin, M Yue Neurocomputing 535, 166-182, 2023 | 39 | 2023 |
| Learning the dynamical response of nonlinear non-autonomous dynamical systems with deep operator neural networks G Lin, C Moya, Z Zhang Engineering Applications of Artificial Intelligence 125, 106689, 2023 | 27* | 2023 |
| A hierarchical framework for demand-side frequency control C Moya, W Zhang, J Lian, K Kalsi 2014 American Control Conference, 52-57, 2014 | 27 | 2014 |
| DAE-PINN: a physics-informed neural network model for simulating differential algebraic equations with application to power networks C Moya, G Lin Neural Computing and Applications 35 (5), 3789-3804, 2023 | 26 | 2023 |
| Developing correlation indices to identify coordinated cyber‐attacks on power grids C Moya, J Wang IET Cyber‐Physical Systems: Theory & Applications 3 (4), 178-186, 2018 | 20 | 2018 |
| Semantic analysis framework for protecting the power grid against monitoring‐control attacks J Wang, G Constante, C Moya, J Hong IET Cyber‐Physical Systems: Theory & Applications 5 (1), 119-126, 2020 | 17* | 2020 |
| Distributed smart grid asset control strategies for providing ancillary services K Kalsi, W Zhang, J Lian, LD Marinovici, C Moya, JE Dagle Pacific Northwest National Lab.(PNNL), Richland, WA (United States), 2013 | 14 | 2013 |
| Frequency responsive demand in US western power system model MA Elizondo, K Kalsi, CM Calderon, W Zhang 2015 IEEE Power & Energy Society General Meeting, 1-5, 2015 | 13 | 2015 |
| Deepgraphonet: A deep graph operator network to learn and zero-shot transfer the dynamic response of networked systems Y Sun, C Moya, G Lin, M Yue IEEE Systems Journal, 1-11, 2023 | 11 | 2023 |
| Fed-deeponet: Stochastic gradient-based federated training of deep operator networks C Moya, G Lin Algorithms 15 (9), 325, 2022 | 10 | 2022 |
| Deep operator learning-based surrogate models with uncertainty quantification for optimizing internal cooling channel rib profiles I Sahin, C Moya, A Mollaali, G Lin, G Paniagua International Journal of Heat and Mass Transfer 219, 124813, 2024 | 8 | 2024 |
| On approximating the dynamic response of synchronous generators via operator learning: A step towards building deep operator-based power grid simulators C Moya, G Lin, T Zhao, M Yue arXiv preprint arXiv:2301.12538, 2023 | 8 | 2023 |
| Defensing against measurement attacks on sub-transmission level C Moya, CC Sun, J Wang, CC Liu 2016 IEEE Power and Energy Society General Meeting (PESGM), 1-5, 2016 | 5 | 2016 |
| Attack path reconstruction from adverse consequences on power grids with a focus on monitoring-layer attacks JK Wang, C Moya 2016 Joint Workshop on Cyber-Physical Security and Resilience in Smart Grids …, 2016 | 5 | 2016 |
| NSGA-PINN: a multi-objective optimization method for physics-informed neural network training B Lu, C Moya, G Lin Algorithms 16 (4), 194, 2023 | 4 | 2023 |
| Semantic-based detection architectures against monitoring-control attacks in power grids G Constante, C Moya, J Wang 2019 IEEE International Conference on Communications, Control, and Computing …, 2019 | 4 | 2019 |
| A cyber-physical testbed design for the electric power grid Z O'Toole, C Moya, C Rubin, A Schnabel, J Wang 2019 North American Power Symposium (NAPS), 1-5, 2019 | 4 | 2019 |
| D2no: Efficient handling of heterogeneous input function spaces with distributed deep neural operators Z Zhang, C Moya, L Lu, G Lin, H Schaeffer Computer Methods in Applied Mechanics and Engineering 428, 117084, 2024 | 3 | 2024 |
| Accelerating approximate thompson sampling with underdamped langevin monte carlo H Zheng, W Deng, C Moya, G Lin arXiv preprint arXiv:2401.11665, 2024 | 3 | 2024 |
