1 citations · 2 across the 6 of their papers we have counts for
6 papers
Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression
Max Kreider, John Harlim, Daning Huang
Accurate prediction of complex dynamical systems from noisy measurements remains a significant challenge in scientific computing. Kernel ridge regression learning strategies are of…
A model-free method for discovering symmetry in differential equations
Max Kreider, John Harlim, Daning Huang
Symmetry in differential equations reveals invariances and offers a powerful means to reduce model complexity. Lie group analysis characterizes these symmetries through infinitesim…
Efficient MPC-Based Energy Management System for Secure and Cost-Effective Microgrid Operations
Hanyang He, John Harlim, Daning Huang +1
Model predictive control (MPC)-based energy management systems (EMS) are essential for ensuring optimal, secure, and stable operation in microgrids with high penetrations of distri…
A reference frame-based microgrid primary control for ensuring global convergence to a periodic orbit
Xinyuan Jiang, Constantino M. Lagoa, Daning Huang +1
Power systems with a high penetration of renewable generation are vulnerable to frequency oscillation and voltage instability. Traditionally, the stability of power systems is cons…
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…