5 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 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…