Extended Dynamic Mode Decomposition

Also known as EDMD

Latest papers

8 papers1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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 GrasevExtended Dynamic Mode Decomposition Model Predictive Control
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 8, 2026J/K move · Enter open · S save

Bilevel optimization for data-driven learning of Koopman embeddings using kernel-based autoencoders

Joel-Pascal Ntwali N'konzi, Feliks Nüske, Stefan Klus

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

Extended Dynamic Mode Decomposition Koopman Operator Learning Latent Dynamics Modeling Kernel Methods

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Koopman operator theory provides a linear framework for analyzing nonlinear dynamical systems and has become a major tool for data-driven modeling. A central challenge, however, is that finite-dimensional approximations computed by methods such as extended dynamic mode decomposition (EDMD) require the dictionary to be specified a priori. Recent machine-learning approaches address this limitation by learning the dictionary from data, predominantly using artificial neural network (ANN) autoencoder architectures. Although kernel methods offer an alternative with greater interpretability and tractability for theoretical analysis, they have received little attention in this setting. We introduce extended dynamic mode decomposition with kernel-based dictionary learning (EDMD-kDL), a kernel-based method for learning finite-dimensional Koopman embeddings directly from data. The method combines ideas from collocation methods and bilevel optimization to simultaneously learn a kernel dictionary and the corresponding Koopman approximation. We evaluate EDMD-kDL against state-of-the-art ANN-based approaches on a range of numerical experiments, including global sea-surface-temperature forecasting and learning directly from video data. Across all tested settings, EDMD-kDL achieves performance comparable to or better than the ANN-based methods. Moreover, in contrast to standard kernel methods, the proposed approach is scalable to large datasets by design since the size of the required kernel matrices depends on the number of collocation points rather than the size of the training dataset.