6 citations · 26 across the 11 of their papers we have counts for
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cs.LG2023★ 6 cited
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…
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.LG2021★ 1 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…