The Adaptive Spectral Koopman Method for Dynamical Systems*

被引:4
作者
Li, Bian [1 ]
Ma, Yian [2 ,3 ]
Kutz, J. Nathan [4 ]
Yang, Xiu [1 ]
机构
[1] Lehigh Univ, Dept Ind & Syst Engn, Bethlehem, PA 18015 USA
[2] Univ Calif San Diego, Haliciogulu Data Sci Inst, La Jolla, CA 92093 USA
[3] Univ Calif San Diego, Dept Comp Sci & Engn, La Jolla, CA 92093 USA
[4] Univ Washington, Dept Appl Math, Seattle, WA 98195 USA
基金
美国国家科学基金会;
关键词
Key words; dynamical system; Koopman operator; spectral -collocation method; SPARSE GRID METHODS; MODE DECOMPOSITION;
D O I
10.1137/22M1487941
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
Dynamical systems have a wide range of applications in mechanics, electrical engineering, chemistry, and so on. In this work, we propose the adaptive spectral Koopman (ASK) method to solve nonlinear autonomous dynamical systems. This novel numerical method leverages the spectralcollocation (i.e., pseudospectral) method and properties of the Koopman operator to obtain the solution of a dynamical system. Specifically, this solution is represented as a linear combination of the multiplication of the Koopman operator's eigenfunctions and eigenvalues, and these eigenpairs are approximated by the spectral method. Unlike conventional time evolution algorithms such as Euler's scheme and the Runge--Kutta scheme, ASK is mesh free and hence is more flexible when evaluating the solution. Numerical experiments demonstrate high accuracy of ASK for solving one-, two-, and three-dimensional dynamical systems.
引用
收藏
页码:1523 / 1551
页数:29
相关论文
共 50 条
[41]   Optimal Stabilization of Adaptive Dynamical Systems with Distributed Parameters: II [J].
V. Yu. Tertychnyi-Dauri .
Differential Equations, 2001, 37 :1618-1626
[42]   Chronos-Koopman spectral analysis of bidimensional turbulent flows [J].
Auliel, Maria Ines ;
Cammilleri, Ada ;
Mininni, Pablo D. ;
Artana, Guillermo O. .
EXPERIMENTS IN FLUIDS, 2022, 63 (05)
[43]   Data-driven spectral decomposition and forecasting of ergodic dynamical systems [J].
Giannakis, Dimitrios .
APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS, 2019, 47 (02) :338-396
[44]   Koopman Operator Applications in Signalized Traffic Systems [J].
Ling, Esther ;
Zheng, Liyuan ;
Ratliff, Lillian J. ;
Coogan, Samuel .
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2022, 23 (04) :3214-3225
[45]   Lyapunov's second method for random dynamical systems [J].
Arnold, L ;
Schmalfuss, B .
JOURNAL OF DIFFERENTIAL EQUATIONS, 2001, 177 (01) :235-265
[46]   On a stability analysis method of dynamical systems with numerical tests [J].
Migdalovici, Marcel ;
Baran, Daniela .
BSG PROCEEDINGS 16, 2009, 16 :99-+
[47]   DATA DRIVEN KOOPMAN SPECTRAL ANALYSIS IN VANDERMONDE-CAUCHY FORM VIA THE DFT: NUMERICAL METHOD AND THEORETICAL INSIGHTS [J].
Drmac, Zlatko ;
Mezic, Igor ;
Mohr, Ryan .
SIAM JOURNAL ON SCIENTIFIC COMPUTING, 2019, 41 (05) :A3118-A3151
[48]   Estimating Koopman Invariant Subspaces of Excited Systems Using Artificial Neural Networks [J].
Bonnert, Marcel ;
Konigorski, Ulrich .
IFAC PAPERSONLINE, 2020, 53 (02) :1156-1162
[49]   Kernel Learning for Data-Driven Spectral Analysis of Koopman Operators [J].
Takeishi, Naoya .
ASIAN CONFERENCE ON MACHINE LEARNING, VOL 101, 2019, 101 :956-971
[50]   Applied Koopman operator theory for power systems technology [J].
Susuki, Yoshihiko ;
Mezic, Igor ;
Raak, Fredrik ;
Hikihara, Takashi .
IEICE NONLINEAR THEORY AND ITS APPLICATIONS, 2016, 7 (04) :430-459