Pages that link to "Item:Q4644309"
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The following pages link to Extended dynamic mode decomposition with dictionary learning: A data-driven adaptive spectral decomposition of the Koopman operator (Q4644309):
Displaying 50 items.
- A data-driven approximation of the koopman operator: extending dynamic mode decomposition (Q897161) (← links)
- Data-driven closures for stochastic dynamical systems (Q2000433) (← links)
- Geometric considerations of a good dictionary for Koopman analysis of dynamical systems: cardinality, ``primary eigenfunction,'' and efficient representation (Q2025462) (← links)
- Low-rank dynamic mode decomposition: an exact and tractable solution (Q2062877) (← links)
- Data-driven operator theoretic methods for phase space learning and analysis (Q2083237) (← links)
- Koopman-based spectral clustering of directed and time-evolving graphs (Q2096996) (← links)
- Lift \& learn: physics-informed machine learning for large-scale nonlinear dynamical systems (Q2115511) (← links)
- Data-driven approximation of the Koopman generator: model reduction, system identification, and control (Q2115518) (← links)
- Extended dynamic mode decomposition for inhomogeneous problems (Q2132641) (← links)
- Data-driven eigensolution analysis based on a spatio-temporal Koopman decomposition, with applications to high-order methods (Q2136484) (← links)
- Kernel embedding based variational approach for low-dimensional approximation of dynamical systems (Q2237840) (← links)
- Variational approach for learning Markov processes from time series data (Q2303757) (← links)
- Nonlinear observability via Koopman analysis: characterizing the role of symmetry (Q2663863) (← links)
- Learning nonlinear state-space models using autoencoders (Q2665158) (← links)
- Koopman Operator Family Spectrum for Nonautonomous Systems (Q4562415) (← links)
- On Matching, and Even Rectifying, Dynamical Systems through Koopman Operator Eigenfunctions (Q4686615) (← links)
- Data-Driven Model Predictive Control using Interpolated Koopman Generators (Q4983502) (← links)
- On explaining the surprising success of reservoir computing forecaster of chaos? The universal machine learning dynamical system with contrast to VAR and DMD (Q4983648) (← links)
- Sparsity-promoting algorithms for the discovery of informative Koopman-invariant subspaces (Q4987934) (← links)
- On Koopman mode decomposition and tensor component analysis (Q4993697) (← links)
- Data-driven inference of high-accuracy isostable-based dynamical models in response to external inputs (Q5000884) (← links)
- Deep learning models for global coordinate transformations that linearise PDEs (Q5014841) (← links)
- Numerical methods to evaluate Koopman matrix from system equations* (Q5048531) (← links)
- Koopman analysis of quantum systems* (Q5057844) (← links)
- Modern Koopman Theory for Dynamical Systems (Q5075835) (← links)
- Reduced Operator Inference for Nonlinear Partial Differential Equations (Q5088794) (← links)
- Generative Stochastic Modeling of Strongly Nonlinear Flows with Non-Gaussian Statistics (Q5097837) (← links)
- On the Koopman Operator of Algorithms (Q5109369) (← links)
- Time-Delay Observables for Koopman: Theory and Applications (Q5109371) (← links)
- Physics-Informed Probabilistic Learning of Linear Embeddings of Nonlinear Dynamics with Guaranteed Stability (Q5109771) (← links)
- Prediction Accuracy of Dynamic Mode Decomposition (Q5112644) (← links)
- Kernel methods for detecting coherent structures in dynamical data (Q5213519) (← links)
- Koopman operator and its approximations for systems with symmetries (Q5242065) (← links)
- Discovery of Nonlinear Multiscale Systems: Sampling Strategies and Embeddings (Q5382442) (← links)
- Linearly Recurrent Autoencoder Networks for Learning Dynamics (Q5383200) (← links)
- Two methods to approximate the Koopman operator with a reservoir computer (Q5858750) (← links)
- Modes of Homogeneous Gradient Flows (Q5860347) (← links)
- Residual dynamic mode decomposition: robust and verified Koopmanism (Q5871684) (← links)
- Koopman analysis of nonlinear systems with a neural network representation (Q6043737) (← links)
- DRIPS: a framework for dimension reduction and interpolation in parameter space (Q6048419) (← links)
- Transformers for modeling physical systems (Q6055222) (← links)
- Regression-Based Projection for Learning Mori–Zwanzig Operators (Q6084965) (← links)
- Deep Koopman model predictive control for enhancing transient stability in power grids (Q6089850) (← links)
- CD-ROM: complemented deep -- reduced order model (Q6094649) (← links)
- A dynamic mode decomposition based reduced-order model for parameterized time-dependent partial differential equations (Q6101657) (← links)
- The mpEDMD Algorithm for Data-Driven Computations of Measure-Preserving Dynamical Systems (Q6108137) (← links)
- Parsimony as the ultimate regularizer for physics-informed machine learning (Q6117148) (← links)
- Koopman operator learning using invertible neural networks (Q6126575) (← links)
- Learning to Forecast Dynamical Systems from Streaming Data (Q6168204) (← links)
- Koopman Operator Inspired Nonlinear System Identification (Q6171206) (← links)
