Koopman Operator, Geometry, and Learning of Dynamical Systems

2021/07/20 by Igor Mezić · 48 citations
Computer Science · Mathematics · Physics and Astronomy · #Computer science #Dynamical systems theory #Geometry #Mathematics #Model Reduction and Neural Networks #Neural Networks and Applications #Operator (biology) #Physics #Quantum mechanics

paper · pdf · doi:10.1090/noti2306

published in Notices of the American Mathematical Society 68(07), 1 (American Mathematical Society)

openalex publication_date 2021/07/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04

Abstract

Koopman operator theory has recently emerged as one of the main candidates for machine learning of dynamical processes. In this article, we briefly describe its history and the current focus, setting it within the new concept of dynamic process representation.

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