4 citations · 6 across the 4 of their papers we have counts for
4 papers
Learning Networked Dynamical System Models with Weak Form and Graph Neural Networks
Yin Yu, Daning Huang, Seho Park +1
This paper presents a sequence of two approaches for the data-driven control-oriented modeling of networked systems, i.e., the systems that involve many interacting dynamical compo…
Learning Coarse-Grained Dynamics on Graph
Yin Yu, John Harlim, Daning Huang +1
We consider a Graph Neural Network (GNN) non-Markovian modeling framework to identify coarse-grained dynamical systems on graphs. Our main idea is to systematically determine the G…
Learning Koopman Operators with Control Using Bi-level Optimization
Daning Huang, Muhammad Bayu Prasetyo, Yin Yu +1
The accurate modeling and control of nonlinear dynamical effects are crucial for numerous robotic systems. The Koopman formalism emerges as a valuable tool for linear control desig…
PIDGeuN: Graph Neural Network-Enabled Transient Dynamics Prediction of Networked Microgrids Through Full-Field Measurement
Yin Yu, Xinyuan Jiang, Daning Huang +1
A Physics-Informed Dynamic Graph Neural Network (PIDGeuN) is presented to accurately, efficiently and robustly predict the nonlinear transient dynamics of microgrids in the presenc…