Results 1 to 10 of about 186,289 (275)
Extended-Kalman-filter-based dynamic mode decomposition for simultaneous system identification and denoising. [PDF]
PLoS ONE, 2019 A new dynamic mode decomposition (DMD) method is introduced for simultaneous system identification and denoising in conjunction with the adoption of an extended Kalman filter algorithm.
Taku Nonomura +2 more
doaj +7 more sources
On Convergence of Extended Dynamic Mode Decomposition to the Koopman Operator [PDF]
Journal of Nonlinear Science, 2017 Extended Dynamic Mode Decomposition (EDMD) is an algorithm that approximates the action of the Koopman operator on an $N$-dimensional subspace of the space of observables by sampling at $M$ points in the state space.
Korda, Milan, Mezić, Igor
core +5 more sources
Extending the extended dynamic mode decomposition with latent observables: the latent EDMD framework [PDF]
Machine Learning: Science and Technology, 2023 Bernard O Koopman proposed an alternative view of dynamical systems based on linear operator theory, in which the time evolution of a dynamical system is analogous to the linear propagation of an infinite-dimensional vector of observables.
Said Ouala +4 more
doaj +4 more sources
Kinetically Consistent Coarse Graining Using Kernel-Based Extended Dynamic Mode Decomposition. [PDF]
J Chem Theory ComputIn this paper, we show how kernel-based models for the Koopman generator -- the gEDMD method -- can be used to identify coarse-grained dynamics on reduced variables, which retain the slowest transition timescales of the original dynamics. The centerpiece of this study is a learning method to identify an effective diffusion in coarse-grained space ...
Nateghi V, Nüske F.
europepmc +5 more sources
Extended dynamic mode decomposition for inhomogeneous problems [PDF]
Journal of Computational Physics, 2021 Dynamic mode decomposition (DMD) is a powerful data-driven technique for construction of reduced-order models of complex dynamical systems. Multiple numerical tests have demonstrated the accuracy and efficiency of DMD, but mostly for systems described by partial differential equations (PDEs) with homogeneous boundary conditions.
Hannah Lu, Daniel M. Tartakovsky
openaire +4 more sources
Mathematics, 2022 Extended Dynamic Mode Decomposition (EDMD) allows an approximation of the Koopman operator to be derived in the form of a truncated (finite dimensional) linear operator in a lifted space of (nonlinear) observable functions.
Camilo Garcia-Tenorio +1 more
doaj +3 more sources
Automation, 2023 This paper provides the theoretical foundation for the approximation of the regions of attraction in hyperbolic and polynomial systems based on the eigenfunctions deduced from the data-driven approximation of the Koopman operator.
Camilo Garcia-Tenorio +3 more
doaj +3 more sources
Efficient Nonlinear Model Predictive Control of Automated Vehicles
Mathematics, 2022 In this paper, an efficient model predictive control (MPC) of velocity tracking of automated vehicles is proposed, in which a reference signal is given a priori.
Shuyou Yu +5 more
doaj +1 more source
Extended dynamic mode decomposition for cyclic macroeconomic data
Data Science in Finance and Economics, 2022 <abstract><p>We apply methods from the Koopman operator theory, Extended Dynamic Mode Decomposition and machine learning in the study of business cycle models. We use a simple non-linear dynamical system whose main merit is that in the appropriate parameter space sector predicts intrinsically business cycles which in the phase space are ...
John Leventides +2 more
openaire +2 more sources
Mathematics, 2021 The extended dynamic mode decomposition algorithm is a tool for accurately approximating the point spectrum of the Koopman operator. This algorithm provides an approximate linear expansion of non-linear discrete-time systems, which can be useful for ...
Camilo Garcia-Tenorio +3 more
doaj +1 more source