Modern Koopman Theory for Dynamical Systems

The field of dynamical systems is being transformed by the mathematical tools and algorithms emerging from modern computing and data science. First-principles derivations and asymptotic reductions are giving way to data-driven approaches that formulate models in operator-theoretic or probabilistic frameworks. Koopman spectral theory has emerged as a dominant perspective over the past decade, in which nonlinear dynamics are represented in terms of an infinite-dimensional linear operator acting on the space of all possible measurement functions of the system. This linear representation of nonlinear dynamics has tremendous potential to enable the prediction, estimation, and control of nonlinear systems with standard textbook methods developed for linear systems. However, obtaining finite-dimensional coordinate systems and embeddings in which the dynamics appear approximately linear remains a central open challenge. The success of Koopman analysis is due primarily to three key factors: (1) there exists rigorous theory connecting it to classical geometric approaches for dynamical systems; (2) the approach is formulated in terms of measurements, making it ideal for leveraging big data and machine learning techniques; and (3) simple, yet powerful numerical algorithms, such as the dynamic mode decomposition (DMD), have been developed and extended to reduce Koopman theory to practice in real-world applications. In this review, we provide an overview of modern Koopman operator theory, describing recent theoretical and algorithmic developments and highlighting these methods with a diverse range of applications. We also discuss key advances and challenges in the rapidly growing field of machine learning that are likely to drive future developments and significantly transform the theoretical landscape of dynamical systems.

Automated summary

The field of dynamical systems is undergoing a transformation due to the emergence of mathematical tools and algorithms from modern computing and data science. Data-driven approaches that use operator-theoretic or probabilistic frameworks are replacing first-principles derivations and asymptotic reductions. The Koopman spectral theory, which represents nonlinear dynamics using an infinite-dimensional linear operator, has the potential to enable the prediction, estimation, and control of nonlinear systems with standard textbook methods developed for linear systems. However, a challenge remains in obtaining finite-dimensional coordinate systems and embeddings that approximately linearize the dynamics. The success of Koopman analysis is attributed to its rigorous theoretical connections, measurement-based approach suitable for leveraging big data and machine learning techniques, and the development of simple yet powerful numerical algorithms.