Pages that link to "Item:Q5109371"

The following pages link to Time-Delay Observables for Koopman: Theory and Applications (Q5109371):

Displaying 20 items.

• Koopman operator framework for time series modeling and analysis (Q2022703) (← links)
• Critical time for the observability of Kolmogorov-type equations (Q2028049) (← links)
• Mesoscale informed parameter estimation through machine learning: a case-study in fracture modeling (Q2125026) (← links)
• Bounded nonlinear forecasts of partially observed geophysical systems with physics-constrained deep learning (Q2688065) (← links)
• Delay Kaldor–Kalecki Model Revisited (Q4555254) (← links)
• Sparsity-promoting algorithms for the discovery of informative Koopman-invariant subspaces (Q4987934) (← links)
• On Koopman mode decomposition and tensor component analysis (Q4993697) (← links)
• (Q4998931) (← links)
• Data-Driven Learning for the Mori--Zwanzig Formalism: A Generalization of the Koopman Learning Framework (Q5023533) (← links)
• Modern Koopman Theory for Dynamical Systems (Q5075835) (← links)
• Residual dynamic mode decomposition: robust and verified Koopmanism (Q5871684) (← links)
• Mitigating Model Error via a Multimodel Method and Application to Tropical Intraseasonal Oscillations (Q6063072) (← links)
• The mpEDMD Algorithm for Data-Driven Computations of Measure-Preserving Dynamical Systems (Q6108137) (← links)
• SPADE4: sparsity and delay embedding based forecasting of epidemics (Q6168035) (← links)
• Rigorous data‐driven computation of spectral properties of Koopman operators for dynamical systems (Q6180710) (← links)
• A Koopman-Takens theorem: linear least squares prediction of nonlinear time series (Q6536643) (← links)
• The spatiotemporal coupling in delay-coordinates dynamic mode decomposition (Q6571520) (← links)
• A LAPACK implementation of the dynamic mode decomposition (Q6604151) (← links)
• Machine learning methods for reduced order modeling (Q6629175) (← links)
• Dynamics-preserving compression for modal flow analysis (Q6663216) (← links)