Extended Dynamic Mode Decomposition

Also known as EDMD

Latest papers

9 papers2 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Data-driven Random-feature Approximation of Koopman Generators of Nonlinear Continuous-time Systems
Oct 29, 2026Ana Čukarska, Maximilian Gollwitzer, Viola Muning Li +3Koopman Operator Learning Nonlinear System Identification
School of Computation, Information and Technology, Technical University of Munich, Germany · Munich Data Science Institute · Interdisciplinary Center for Scientific Computing (IWR) +1
Bilevel optimization for data-driven learning of Koopman embeddings using kernel-based autoencoders
Oct 8, 2026Joel-Pascal Ntwali N'konzi, Feliks Nüske, Stefan KlusExtended Dynamic Mode Decomposition Koopman Operator Learning
Maxwell Institute for Mathematical Sciences, The University of Edinburgh & Heriot–Watt University, Edinburgh, United Kingdom · Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany · LAAS-CNRS, Université de Toulouse, Toulouse, France +1
Control-Oriented Learning for Dynamic Tracking and Stability Analysis of Soft Pneumatic Actuators
Aug 25, 2026Nithin S. Kumar, Eric J. BarthExtended Dynamic Mode Decomposition Soft Pneumatic Actuators
Department of Mechanical Engineering, Vanderbilt University, Nashville TN 37215
Cluster-Weighted EDMD
Jul 14, 2026Lorenzo Tomaz, Judd Rosenblatt, Flavio Kicis +2Koopman Operator Learning Latent Dynamics Modeling
AE Studio
Koopman operator theory: fundamentals, control, and applications
Jul 2, 2026Igor Mezić, Jorge Cortés, Karl Worthmann +2Surrogate Modeling Koopman Operator Learning
Department of Mechanical Engineering, University of California, Santa Barbara, USA · Department of Mechanical and Aerospace Engineering, University of California, San Diego, USA · Optimization-based Control Group, TU Ilmenau, Germany +2
Wavelet-Based Observables for Koopman Analysis: An Extended Dynamic Mode Decomposition Framework
May 14, 2026Cankat Tilki, Serkan GugercinDynamical Systems Koopman Operator Learning
Department of Mathematics, Virginia Tech, Blacksburg, VA - 24061.
On Data-Driven Koopman Representations of Nonlinear Delay Differential Equations
Apr 3, 2026Santosh Mohan Rajkumar, Dibyasri Barman, Kumar Vikram Singh +1Extended Dynamic Mode Decomposition Dynamical Systems
Department of Mechanical and Aerospace Engineering, The Ohio State University, Columbus, OH, USA. · Department of Chemical, Paper, and Biomedical Engineering at Miami University, Oxford, OH, USA. · Department of Mechanical and Manufacturing Engineering at Miami University, Oxford, OH, USA.
Koopman-Based Nonlinear Identification and Model Predictive Control of a Turbofan Engine
Apr 2, 2026David GrasevAdaptive MPC Extended Dynamic Mode Decomposition
Department of Aviation Technology, University of Defence, Brno, Czech Republic.
Koopman Operator Identification of Model Parameter Trajectories for Temporal Domain Generalization (KOMET)
Mar 27, 2026Randy C. Hoover, Jacob James, Paul May +1Extended Dynamic Mode Decomposition Drifting Models
Department of Electrical Engineering and Computer Science (EECS), South Dakota Mines, Rapid City, SD 57702, USA · Data Science and Engineering Program within EECS, South Dakota Mines, Rapid City, SD 57702, USA · Department of Mathematics, South Dakota Mines, Rapid City, SD 57702, USA

Oct 29, 2026J/K move · Enter open · S save

Data-driven Random-feature Approximation of Koopman Generators of Nonlinear Continuous-time Systems

Ana Čukarska, Maximilian Gollwitzer, Viola Muning Li, Vladyslav Fediukov+2

School of Computation, Information and Technology, Technical University of Munich, Germany · Munich Data Science Institute · Interdisciplinary Center for Scientific Computing (IWR)+1

Koopman Operator Learning Nonlinear System Identification Extended Dynamic Mode Decomposition Random Feature Methods

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Learning continuous-time dynamical systems from data is an active area of research. The Koopman formalism is often used here, because it is a flexible approach that is informative about the system's fundamental properties. The formalism represents dynamical systems through linear operators acting on a suitable function space. A practical difficulty arising in the numerical approximation of Koopman operators is the choice of a ``dictionary'', meaning an effective finite-dimensional representation of the function space. In this paper, we focus on learning dynamical systems in continuous-time, and so we consider numerical approximations of the Koopman generator instead of the Koopman operator. Our main contribution is a data-driven random-feature method to obtain a parsimonious and effective function representation for Galerkin approximation methods such as gEDMD. The representation we construct is data-informed, which allows us to improve upon data-agnostic methods. We empirically demonstrate the suitability of our method for forecasting tasks and system analysis. The numerical experiments show that our approach is accurate, and faster than iterative optimization. Further, we prove convergence of our approximation of the vector field and the resulting trajectories. In addition to the numerical results, we also make theoretical contributions, including a general convergence criterion for the semigroup generated by the gEDMD approximation and a counterexample showing that gEDMD does not generally preserve exponential boundedness. With our contribution, we advance the frontier of efficiently learning continuous-time dynamics from data, towards interpretable and mathematically robust modelling based on the Koopman framework.