Results 11 to 20 of about 6,773,813 (277)
On the Effect of Quantization on Extended Dynamic Mode Decomposition [PDF]
2025 American Control Conference (ACC)Extended Dynamic Mode Decomposition (EDMD) is a widely used data-driven algorithm for estimating the Koopman Operator. EDMD extends Dynamic Mode Decomposition (DMD) by lifting the snapshot data using nonlinear dictionary functions before performing the estimation.
Debdipta Goswami, Dipankar Maity
exaly +5 more sources
Group-convolutional extended dynamic mode decomposition
Physica D: Nonlinear PhenomenaThis paper explores the integration of symmetries into the Koopman-operator framework for the analysis and efficient learning of equivariant dynamical systems using a group-convolutional approach. Approximating the Koopman operator by finite-dimensional surrogates, e.g., via extended dynamic mode decomposition (EDMD), is challenging for high ...
Karl Worthmann +2 more
exaly +4 more sources
Nonlinear Theory and Its Applications IEICE, 2021 Nonlinear phenomena can be analyzed via linear techniques using operator-theoretic approaches. Data-driven method called the extended dynamic mode decomposition (EDMD) and its variants, which approximate the Koopman operator associated with the nonlinear phenomena, have been rapidly developing by incorporating machine learning methods.
Sho Shirasaka +2 more
exaly +4 more sources
A concise introduction to Koopman operator theory and the Extended Dynamic Mode Decomposition [PDF]
CoRR13 pages, subject introduction ...
Christophe Patyn, Geert Deconinck
core +5 more sources
Dictionary learning in Extended Dynamic Mode Decomposition using a reservoir computer [PDF]
, 2019 We aim at improving extended dynamic mode decomposition that allows to linearize nonlinear systems.Indeed, the EDMD algorithm provides a finite-dimensional representation of the Koopman operator.Finally, the reservoir computer is trained to produce an efficient ...
Gulina, Marvyn; id_orcid 0000-0001-6420-6911 +1 more
core +6 more sources
Data-Driven MPC With Stability Guarantees Using Extended Dynamic Mode Decomposition
IEEE Transactions on Automatic Control18 pages, 3 ...
Karl Worthmann +2 more
exaly +4 more sources
Extended dynamic mode decomposition for model reduction in fluid dynamics simulations [PDF]
Physics of FluidsHigh computational cost and storage/memory requirements of fluid dynamics simulations constrain their usefulness as a predictive tool. Reduced-order models (ROMs) provide a viable solution to this challenge by extracting the key underlying dynamics of a complex system directly from data. We investigate the efficacy and robustness of an extended dynamic
Giulia Libero +3 more
openaire +3 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 +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 with Learned Koopman Eigenfunctions for Prediction and Control [PDF]
2020 American Control Conference (ACC), 2020 This paper presents a novel learning framework to construct Koopman eigenfunctions for unknown, nonlinear dynamics using data gathered from experiments. The learning framework can extract spectral information from the full nonlinear dynamics by learning the eigenvalues and eigenfunctions of the associated Koopman operator.
Folkestad, Carl +5 more
openaire +5 more sources