The field of dynamical systems is being transformed by the mathematical tools and
algorithms emerging from modern computing and data science. First-principles derivations …

Koopman operator theory has proven to be a promising approach to nonlinear system
identification and global linearization. For nearly a century, there had been no efficient …

Learning and synthesizing stabilizing controllers for unknown nonlinear control systems is a
challenging problem for real-world and industrial applications. Koopman operator theory …

Dynamic Mode Decomposition (DMD) is a popular data-driven analysis technique used to
decompose complex, nonlinear systems into a set of modes, revealing underlying patterns …

Abstract The Koopman and Perron Frobenius transport operators are fundamentally
changing how we approach dynamical systems, providing linear representations for even …

This article develops data-driven methods to identify eigenfunctions of the Koopman
operator associated with a dynamical system and subspaces that are invariant under the …

In this paper, we propose a novel approach to compute the Koopman operator from sparse
time series data. In recent years there have been considerable interests in operator theoretic …

In recent years data-driven analysis of dynamical systems has attracted a lot of attention and
transfer operator techniques, namely, Perron-Frobenius and Koopman operators are being …

Sensors in buildings are used for a wide variety of applications such as monitoring air
quality, contaminants, indoor temperature, and relative humidity. These are used for …

Optimal sensor placement is an important yet unsolved problem in control theory. In
biological organisms, genetic activity is often highly nonlinear, making it difficult to design …