受强制性开放获取政策约束的文章 - Christian Moya了解详情
无法在其他位置公开访问的文章：6 篇
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
强制性开放获取政策： US Department of Energy
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
强制性开放获取政策： US National Science Foundation
Bayesian, multifidelity operator learning for complex engineering systems–a position paper
C Moya, G Lin
Journal of Computing and Information Science in Engineering 23 (6), 2023
强制性开放获取政策： US National Science Foundation, US Department of Energy
Smart resistor for stability improvement of the dc link in turbo-electric aircrafts
K Potty, E Bauer, C Moya, B Sim, J Wang
2019 IEEE Applied Power Electronics Conference and Exposition (APEC), 3346-3352, 2019
强制性开放获取政策： US National Aeronautics and Space Administration
Deeponet based uncertainty quantification for power system dynamics with stochastic loads
K Ye, J Zhao, X Liu, C Moya, G Lin
2023 IEEE Power & Energy Society General Meeting (PESGM), 1-6, 2023
强制性开放获取政策： US National Science Foundation, US Department of Energy
Automatic generation control under single time-delay attack
Z Gao, C Moya, J Wang, M Illindala
2023 IEEE Texas Power and Energy Conference (TPEC), 1-6, 2023
强制性开放获取政策： US Department of Energy
可在其他位置公开访问的文章：8 篇
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
强制性开放获取政策： US National Science Foundation, US Department of Energy
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
强制性开放获取政策： US National Science Foundation, US Department of Energy, US Department of …
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
强制性开放获取政策： US National Science Foundation, US Department of Energy
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
强制性开放获取政策： US National Science Foundation, US Department of Energy
Fed-deeponet: Stochastic gradient-based federated training of deep operator networks
C Moya, G Lin
Algorithms 15 (9), 325, 2022
强制性开放获取政策： US National Science Foundation, US Department of Energy, US Department of …
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
强制性开放获取政策： US National Science Foundation, US Department of Energy
NSGA-PINN: a multi-objective optimization method for physics-informed neural network training
B Lu, C Moya, G Lin
Algorithms 16 (4), 194, 2023
强制性开放获取政策： US National Science Foundation, US Department of Energy
A physics-guided bi-fidelity fourier-featured operator learning framework for predicting time evolution of drag and lift coefficients
A Mollaali, I Sahin, I Raza, C Moya, G Paniagua, G Lin
Fluids 8 (12), 323, 2023
强制性开放获取政策： US National Science Foundation, US Department of Energy
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