41 citations · 42 across the 6 of their papers we have counts for
7 papers
Temporal Forward-Backward Consistency, Not Residual Error, Measures the Prediction Accuracy of Extended Dynamic Mode Decomposition
Masih Haseli, Jorge Cortés
Extended Dynamic Mode Decomposition (EDMD) is a popular data-driven method to approximate the action of the Koopman operator on a linear function space spanned by a dictionary of f…
Stable Reinforcement Learning for Optimal Frequency Control: A Distributed Averaging-Based Integral Approach
Yan Jiang, Wenqi Cui, Baosen Zhang +1
Frequency control plays a pivotal role in reliable power system operations. It is conventionally performed in a hierarchical way that first rapidly stabilizes the frequency deviati…
Control Barrier Function Based Design of Gradient Flows for Constrained Nonlinear Programming
Ahmed Allibhoy, Jorge Cortés
This paper considers the problem of designing a continuous-time dynamical system that solves a constrained nonlinear optimization problem and makes the feasible set forward invaria…
Optimization-Based Safe Stabilizing Feedback with Guaranteed Region of Attraction
Pol Mestres, Jorge Cortés
This paper proposes an optimization with penalty-based feedback design framework for safe stabilization of control affine systems. Our starting point is the availability of a contr…
Global Kinetic Modeling of the Intrabinary Shock in Spider Pulsars
Jorge Cortés, Lorenzo Sironi
Spider pulsars are compact binary systems composed of a millisecond pulsar and a low-mass companion. The relativistic magnetically-dominated pulsar wind impacts onto the companion,…
Selective Inhibition and Recruitment of Linear-Threshold Thalamocortical Networks
Michael McCreesh, Jorge Cortés
Neuroscientific evidence shows that for most brain networks all pathways between cortical regions either pass through the thalamus or a transthalamic parallel route exists for any…
Generalizing Dynamic Mode Decomposition: Balancing Accuracy and Expressiveness in Koopman Approximations
Masih Haseli, Jorge Cortés
This paper tackles the data-driven approximation of unknown dynamical systems using Koopman-operator methods. Given a dictionary of functions, these methods approximate the project…