Center for Advanced Intelligence Project, RIKEN, Tokyo 103-0027, Japan.
LAAS-CNRS, 7 avenue du colonel Roche, F-31400 Toulouse, France.
Chaos. 2022 Dec;32(12):123143. doi: 10.1063/5.0094889.
Koopman and Perron-Frobenius operators for dynamical systems are becoming popular in a number of fields in science recently. Properties of the Koopman operator essentially depend on the choice of function spaces where it acts. Particularly, the case of reproducing kernel Hilbert spaces (RKHSs) is drawing increasing attention in data science. In this paper, we give a general framework for Koopman and Perron-Frobenius operators on reproducing kernel Banach spaces (RKBSs). More precisely, we extend basic known properties of these operators from RKHSs to RKBSs and state new results, including symmetry and sparsity concepts, on these operators on RKBS for discrete and continuous time systems.
近年来,动力系统的 Koopman 和 Perron-Frobenius 算子在许多科学领域变得越来越受欢迎。Koopman 算子的性质本质上取决于它作用的函数空间的选择。特别是,再生核希尔伯特空间 (RKHS) 的情况在数据科学中引起了越来越多的关注。在本文中,我们给出了再生核巴拿赫空间 (RKBS) 上 Koopman 和 Perron-Frobenius 算子的一般框架。更准确地说,我们将这些算子在 RKHS 中的基本已知性质扩展到 RKBS 中,并对离散和连续时间系统的 RKBS 上的这些算子给出了新的结果,包括对称性和稀疏性概念。