5 papers
Koopman Subspace Pruning in Reproducing Kernel Hilbert Spaces via Principal Vectors
Dhruv Shah, Jorge Cortes
Data-driven approximations of the infinite-dimensional Koopman operator rely on finite-dimensional projections, where the predictive accuracy of the resulting models hinges heavily…
A Unified Algebraic Framework for Subspace Pruning in Koopman Operator Approximation via Principal Vectors
Dhruv Shah, Jorge Cortes
Finite-dimensional approximations of the Koopman operator rely critically on identifying nearly invariant subspaces. This invariance proximity can be rigorously quantified via the…
Set-valued regression and cautious suboptimization: From noisy data to optimality
Jaap Eising, Jorge Cortes
This paper deals with the problem of finding suboptimal values of an unknown function on the basis of measured data corrupted by bounded noise. As a prior, we assume that the unkno…
Data-Enabled Predictive Control for Nonlinear Systems Based on a Koopman Bilinear Realization
Zuxun Xiong, Zhenyi Yuan, Keyan Miao +3
This paper extends the Willems' Fundamental Lemma to nonlinear control-affine systems using the Koopman bilinear realization. This enables us to bypass the Extended Dynamic Mode De…
Linear-Threshold Network Models for Describing and Analyzing Brain Dynamics
Michael McCreesh, Erfan Nozari, Jorge Cortes
Over the past two decades, an increasing array of control-theoretic methods have been used to study the brain as a complex dynamical system and better understand its structure-func…