Pages that link to "Item:Q317185"

The following pages link to A kernel-based method for data-driven Koopman spectral analysis (Q317185):

Displaying 45 items.

• 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)
• Physics-Informed Probabilistic Learning of Linear Embeddings of Nonlinear Dynamics with Guaranteed Stability (Q5109771) (← links)
• Discovery of Dynamics Using Linear Multistep Methods (Q5151929) (← links)
• Kernel methods for detecting coherent structures in dynamical data (Q5213519) (← links)
• Scalable Extended Dynamic Mode Decomposition Using Random Kernel Approximation (Q5230606) (← links)
• Quenched stochastic stability for eventually expanding-on-average random interval map cocycles (Q5235022) (← links)
• Online Dynamic Mode Decomposition for Time-Varying Systems (Q5238246) (← links)
• Koopman operator and its approximations for systems with symmetries (Q5242065) (← links)
• Higher Order Dynamic Mode Decomposition (Q5266355) (← links)
• Discovery of Nonlinear Multiscale Systems: Sampling Strategies and Embeddings (Q5382442) (← links)
• Linearly Recurrent Autoencoder Networks for Learning Dynamics (Q5383200) (← links)
• Sparsity Structures for Koopman and Perron--Frobenius Operators (Q5868544) (← links)
• Residual dynamic mode decomposition: robust and verified Koopmanism (Q5871684) (← links)
• Higher Order Extended Dynamic Mode Decomposition Based on the Structured Total Least Squares (Q6039255) (← links)
• Overcoming the timescale barrier in molecular dynamics: Transfer operators, variational principles and machine learning (Q6047503) (← links)
• Data-driven probability density forecast for stochastic dynamical systems (Q6054200) (← links)
• Auxiliary functions as Koopman observables: data-driven analysis of dynamical systems via polynomial optimization (Q6066022) (← links)
• Singular Dynamic Mode Decomposition (Q6076402) (← links)
• Kernel‐based active subspaces with application to computational fluid dynamics parametric problems using the discontinuous Galerkin method (Q6092233) (← links)
• CD-ROM: complemented deep -- reduced order model (Q6094649) (← links)
• Combining dynamic mode decomposition with ensemble Kalman filtering for tracking and forecasting (Q6098248) (← 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)
• Constrained optimized dynamic mode decomposition with control for physically stable systems with exogeneous inputs (Q6117704) (← links)
• Koopman operator learning using invertible neural networks (Q6126575) (← links)
• Neural dynamic mode decomposition for end-to-end modeling of nonlinear dynamics (Q6166776) (← links)
• The Adaptive Spectral Koopman Method for Dynamical Systems (Q6167521) (← links)
• Learning to Forecast Dynamical Systems from Streaming Data (Q6168204) (← links)
• Rigorous data‐driven computation of spectral properties of Koopman operators for dynamical systems (Q6180710) (← links)
• Ensemble forecasts in reproducing kernel Hilbert space family (Q6191535) (← links)
• Reachability of Koopman Linearized Systems Using Random Fourier Feature Observables and Polynomial Zonotope Refinement (Q6487327) (← links)
• A model reduction method for parametric dynamical systems defined on complex geometries (Q6498464) (← links)
• An adaptive method based on local dynamic mode decomposition for parametric dynamical systems (Q6537069) (← links)
• Enhancing predictive capabilities in data-driven dynamical modeling with automatic differentiation: Koopman and neural ODE approaches (Q6554429) (← links)
• The occupation kernel method for nonlinear system identification (Q6555693) (← links)
• Deep learning enhanced dynamic mode decomposition (Q6560590) (← links)
• Koopman dynamic-oriented deep learning for invariant subspace identification and full-state prediction of complex systems (Q6588249) (← links)
• A LAPACK implementation of the dynamic mode decomposition (Q6604151) (← links)
• Approximation of translation invariant Koopman operators for coupled non-linear systems (Q6604821) (← links)
• Machine learning in viscoelastic fluids via energy-based kernel embedding (Q6615024) (← links)
• Data-driven linearization of dynamical systems (Q6617274) (← links)
• Another look at residual dynamic mode decomposition in the regime of fewer snapshots than dictionary size (Q6629751) (← links)