A hierarchical framework for demand-side frequency control. - Christian Moya; Wei Zhang 013; Jianming Lian; Karanjit Kalsi (ACC, 2014)

Cyber-Attacks to Voltage Control Applications via Wide Area Monitoring, Protection and Control System. - Christian Moya; Jiankang Wang (SPSR-SG@CPSWeek, 2017)

Developing a Correlation Index to Identify Coordinated Cyber-Attacks to Power Grids. - Christian Moya; Jiankang Wang (CoRR, 2017)

Application of Correlation Indices on Intrusion Detection Systems: Protecting the Power Grid Against Coordinated Attacks. - Christian Moya; Junho Hong; Jiankang Wang (CoRR, 2018)

Semantic-Based Detection Architectures Against Monitoring-Control Attacks in Power Grids. - Gonzalo E. Constante; Christian Moya; Jiankang Wang (SmartGridComm, 2019)

Developing correlation indices to identify coordinated cyber-attacks on power grids. - Christian Moya; Jiankang Wang (IET Cyper-Phys. Syst.: Theory & Appl., 2019)

Semantic analysis framework for protecting the power grid against monitoring-control attacks. - Jiankang Wang; Gonzalo E. Constante; Christian Moya; Junho Hong (IET Cyper-Phys. Syst.: Theory & Appl., 2020)

Accelerated replica exchange stochastic gradient Langevin diffusion enhanced Bayesian DeepONet for solving noisy parametric PDEs. - Guang Lin 001; Christian Moya; Zecheng Zhang (CoRR, 2021)

Fed-DeepONet: Stochastic Gradient-Based Federated Training of Deep Operator Networks. - Christian Moya; Guang Lin 001 (Algorithms, 2022)

On Learning the Dynamical Response of Nonlinear Control Systems with Deep Operator Networks. - Guang Lin 001; Christian Moya; Zecheng Zhang (CoRR, 2022)

On Approximating the Dynamic Response of Synchronous Generators via Operator Learning: A Step Towards Building Deep Operator-based Power Grid Simulators. - Christian Moya; Guang Lin 001; Tianqiao Zhao; Meng Yue (CoRR, 2023)

Deep Operator Learning-based Surrogate Models with Uncertainty Quantification for Optimizing Internal Cooling Channel Rib Profiles. - Izzet Sahin; Christian Moya; Amirhossein Mollaali; Guang Lin 001; Guillermo Paniagua (CoRR, 2023)

D2NO: Efficient Handling of Heterogeneous Input Function Spaces with Distributed Deep Neural Operators. - Zecheng Zhang; Christian Moya; Lu Lu; Guang Lin 001; Hayden Schaeffer (CoRR, 2023)

A Physics-Guided Bi-Fidelity Fourier-Featured Operator Learning Framework for Predicting Time Evolution of Drag and Lift Coefficients. - Amirhossein Mollaali; Izzet Sahin; Iqrar Raza; Christian Moya; Guillermo Paniagua; Guang Lin 001 (CoRR, 2023)

B-LSTM-MIONet: Bayesian LSTM-based Neural Operators for Learning the Response of Complex Dynamical Systems to Length-Variant Multiple Input Functions. - Zhihao Kong; Amirhossein Mollaali; Christian Moya; Na Lu; Guang Lin 001 (CoRR, 2023)

Learning the dynamical response of nonlinear non-autonomous dynamical systems with deep operator neural networks. - Guang Lin 001; Christian Moya; Zecheng Zhang (Eng. Appl. Artif. Intell., 2023)

DeepONet-grid-UQ: A trustworthy deep operator framework for predicting the power grid's post-fault trajectories. - Christian Moya; Shiqi Zhang 006; Guang Lin 001; Meng Yue (Neurocomputing, 2023)

B-DeepONet: An enhanced Bayesian DeepONet for solving noisy parametric PDEs using accelerated replica exchange SGLD. - Guang Lin 001; Christian Moya; Zecheng Zhang (J. Comput. Phys., 2023)

DAE-PINN: a physics-informed neural network model for simulating differential algebraic equations with application to power networks. - Christian Moya; Guang Lin 001 (Neural Comput. Appl., 2023)

NSGA-PINN: A Multi-Objective Optimization Method for Physics-Informed Neural Network Training. - Binghang Lu; Christian Moya; Guang Lin 001 (Algorithms, 2023)

DeepGraphONet: A Deep Graph Operator Network to Learn and Zero-Shot Transfer the Dynamic Response of Networked Systems. - Yixuan Sun; Christian Moya; Guang Lin 001; Meng Yue (IEEE Syst. J., 2023)

Bayesian, Multifidelity Operator Learning for Complex Engineering Systems-A Position Paper. - Christian Moya; Guang Lin 001 (J. Comput. Inf. Sci. Eng., 2023)

Accelerating Approximate Thompson Sampling with Underdamped Langevin Monte Carlo. - Haoyang Zheng; Wei Deng 002; Christian Moya; Guang Lin 001 (AISTATS, 2024)

Conformalized-DeepONet: A Distribution-Free Framework for Uncertainty Quantification in Deep Operator Networks. - Christian Moya; Amirhossein Mollaali; Zecheng Zhang; Lu Lu; Guang Lin 001 (CoRR, 2024)

An Energy-Based Self-Adaptive Learning Rate for Stochastic Gradient Descent: Enhancing Unconstrained Optimization with VAV method. - Jiahao Zhang 002; Christian Moya; Guang Lin 001 (CoRR, 2024)

DeepONet as a Multi-Operator Extrapolation Model: Distributed Pretraining with Physics-Informed Fine-Tuning. - Zecheng Zhang; Christian Moya; Lu Lu; Guang Lin 001; Hayden Schaeffer (CoRR, 2024)

Bayesian Deep Operator Learning for Homogenized to Fine-Scale Maps for Multiscale PDE. - Zecheng Zhang; Christian Moya; Wing Tat Leung; Guang Lin 001; Hayden Schaeffer (Multiscale Model. Simul., 2024)

A self-adaptive energy-based learning rate for stochastic gradient descent via Vector Auxiliary Variable method. - Jiahao Zhang 002; Christian Moya; Guang Lin 001 (Eng. Appl. Artif. Intell., 2025)

FPINN-deeponet: A physics-informed operator learning framework for multi-term time-fractional mixed diffusion-wave equations. - Binghang Lu; Zhaopeng Hao; Christian Moya; Guang Lin 001 (J. Comput. Phys., 2025)

Conformalized prediction of post-fault voltage trajectories using pre-trained and finetuned attention-driven neural operators. - Amirhossein Mollaali; Gabriel Zufferey; Gonzalo Constante-Flores; Christian Moya; Can Li; Meng Yue; Guang Lin 001 (Neural Networks, 2025)

Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning. - Amirhossein Mollaali; Christian B. Moya; Amanda A. Howard; Alexander Heinlein; Panos Stinis; Guang Lin 001 (CoRR, 2025)

Linear Spatial World Models Emerge in Large Language Models. - Matthieu Tehenan; Christian B. Moya; Tenghai Long; Guang Lin 001 (CoRR, 2025)