6 citations · 26 across the 11 of their papers we have counts for
11 papers
Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations
Young Hyun Cho, Franz Stoll, Will Wei Sun +2
Unexpected shocks recur in global operations, requiring decision rules that adapt as market and operating conditions change. Many operational systems also have hierarchical structu…
Deep Operator Learning-based Surrogate Models with Uncertainty Quantification for Optimizing Internal Cooling Channel Rib Profiles
Izzet Sahin, Christian Moya, Amirhossein Mollaali +2
This paper designs surrogate models with uncertainty quantification capabilities to improve the thermal performance of rib-turbulated internal cooling channels effectively. To cons…
On Approximating the Dynamic Response of Synchronous Generators via Operator Learning: A Step Towards Building Deep Operator-based Power Grid Simulators
Christian Moya, Amirhossein Mollaali, Guang Lin +1
This paper develops an Operator Learning framework for approximating the dynamic response of synchronous generators. The framework can be used to (i) build a neural network-based g…
Federated X-Armed Bandit
Wenjie Li, Qifan Song, Jean Honorio +1
This work establishes the first framework of federated -armed bandit, where different clients face heterogeneous local objective functions defined on the same domain a…
Federated Online Sparse Decision Making
Chi-Hua Wang, Wenjie Li, Guang Cheng +1
This paper presents a novel federated linear contextual bandits model, where individual clients face different K-armed stochastic bandits with high-dimensional decision context and…
Interacting Contour Stochastic Gradient Langevin Dynamics
Wei Deng, Siqi Liang, Botao Hao +2
We propose an interacting contour stochastic gradient Langevin dynamics (ICSGLD) sampler, an embarrassingly parallel multiple-chain contour stochastic gradient Langevin dynamics (C…
DeepONet-Grid-UQ: A Trustworthy Deep Operator Framework for Predicting the Power Grid's Post-Fault Trajectories
Christian Moya, Shiqi Zhang, Meng Yue +1
This paper proposes a new data-driven method for the reliable prediction of power system post-fault trajectories. The proposed method is based on the fundamentally new concept of D…
glassoformer: a query-sparse transformer for post-fault power grid voltage prediction
Yunling Zheng, Carson Hu, Guang Lin +3
We propose GLassoformer, a novel and efficient transformer architecture leveraging group Lasso regularization to reduce the number of queries of the standard self-attention mechani…
On Convergence of Federated Averaging Langevin Dynamics
Wei Deng, Qian Zhang, Yi-An Ma +2
We propose a federated averaging Langevin algorithm (FA-LD) for uncertainty quantification and mean predictions with distributed clients. In particular, we generalize beyond normal…
DAE-PINN: A Physics-Informed Neural Network Model for Simulating Differential-Algebraic Equations with Application to Power Networks
Christian Moya, Guang Lin
Deep learning-based surrogate modeling is becoming a promising approach for learning and simulating dynamical systems. Deep-learning methods, however, find very challenging learnin…
Reinforcement Learning for Traffic Control with Adaptive Horizon
Wentao Chen, Tehuan Chen, Guang Lin
This paper proposes a reinforcement learning approach for traffic control with the adaptive horizon. To build the controller for the traffic network, a Q-learning-based strategy th…