Christian Moya
Orcid: 0000-0003-0180-9285
According to our database1,
Christian Moya
authored at least 26 papers
between 2014 and 2024.
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Bibliography
2024
Bayesian Deep Operator Learning for Homogenized to Fine-Scale Maps for Multiscale PDE.
Multiscale Model. Simul., 2024
Conformalized Prediction of Post-Fault Voltage Trajectories Using Pre-trained and Finetuned Attention-Driven Neural Operators.
CoRR, 2024
Conformalized-DeepONet: A Distribution-Free Framework for Uncertainty Quantification in Deep Operator Networks.
CoRR, 2024
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024
2023
Bayesian, Multifidelity Operator Learning for Complex Engineering Systems-A Position Paper.
J. Comput. Inf. Sci. Eng., December, 2023
DeepGraphONet: A Deep Graph Operator Network to Learn and Zero-Shot Transfer the Dynamic Response of Networked Systems.
IEEE Syst. J., September, 2023
NSGA-PINN: A Multi-Objective Optimization Method for Physics-Informed Neural Network Training.
Algorithms, April, 2023
DAE-PINN: a physics-informed neural network model for simulating differential algebraic equations with application to power networks.
Neural Comput. Appl., February, 2023
B-DeepONet: An enhanced Bayesian DeepONet for solving noisy parametric PDEs using accelerated replica exchange SGLD.
J. Comput. Phys., 2023
DeepONet-grid-UQ: A trustworthy deep operator framework for predicting the power grid's post-fault trajectories.
Neurocomputing, 2023
Learning the dynamical response of nonlinear non-autonomous dynamical systems with deep operator neural networks.
Eng. Appl. Artif. Intell., 2023
B-LSTM-MIONet: Bayesian LSTM-based Neural Operators for Learning the Response of Complex Dynamical Systems to Length-Variant Multiple Input Functions.
CoRR, 2023
A Physics-Guided Bi-Fidelity Fourier-Featured Operator Learning Framework for Predicting Time Evolution of Drag and Lift Coefficients.
CoRR, 2023
D2NO: Efficient Handling of Heterogeneous Input Function Spaces with Distributed Deep Neural Operators.
CoRR, 2023
Deep Operator Learning-based Surrogate Models with Uncertainty Quantification for Optimizing Internal Cooling Channel Rib Profiles.
CoRR, 2023
On Approximating the Dynamic Response of Synchronous Generators via Operator Learning: A Step Towards Building Deep Operator-based Power Grid Simulators.
CoRR, 2023
2022
On Learning the Dynamical Response of Nonlinear Control Systems with Deep Operator Networks.
CoRR, 2022
Fed-DeepONet: Stochastic Gradient-Based Federated Training of Deep Operator Networks.
Algorithms, 2022
2021
Accelerated replica exchange stochastic gradient Langevin diffusion enhanced Bayesian DeepONet for solving noisy parametric PDEs.
CoRR, 2021
2020
Semantic analysis framework for protecting the power grid against monitoring-control attacks.
IET Cyper-Phys. Syst.: Theory & Appl., 2020
2019
IET Cyper-Phys. Syst.: Theory & Appl., 2019
Semantic-Based Detection Architectures Against Monitoring-Control Attacks in Power Grids.
Proceedings of the 2019 IEEE International Conference on Communications, 2019
2018
Application of Correlation Indices on Intrusion Detection Systems: Protecting the Power Grid Against Coordinated Attacks.
CoRR, 2018
2017
CoRR, 2017
Cyber-Attacks to Voltage Control Applications via Wide Area Monitoring, Protection and Control System.
Proceedings of the 2nd Workshop on Cyber-Physical Security and Resilience in Smart Grids, 2017
2014
Proceedings of the American Control Conference, 2014