most citedDeepONet-Grid-UQ: A Trustworthy Deep Operator Framework for Predicting the Power Grid's Post-Fault Trajectories

6 citations · 13 across the 5 of their papers we have counts for

collaborators

5 papers

stat.ML20221 cited

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…

stat.ML20225 cited

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…

math.NA20226 cited

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…

cs.LG2022

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…

cs.LG20211 cited

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…