Pages that link to "Item:Q897161"

The following pages link to A data-driven approximation of the koopman operator: extending dynamic mode decomposition (Q897161):

Displaying 35 items.

• Learning the temporal evolution of multivariate densities via normalizing flows (Q6560595) (← links)
• Data-driven stochastic model for cross-interacting processes with different time scales (Q6561184) (← links)
• Data-driven identification of dynamical models using adaptive parameter sets (Q6561192) (← links)
• Time-series forecasting using manifold learning, radial basis function interpolation, and geometric harmonics (Q6567586) (← links)
• The spatiotemporal coupling in delay-coordinates dynamic mode decomposition (Q6571520) (← links)
• Koopman and Perron-Frobenius operators on reproducing kernel Banach spaces (Q6571536) (← links)
• On principles of emergent organization (Q6571624) (← links)
• Slow invariant manifolds of singularly perturbed systems via physics-informed machine learning (Q6573172) (← links)
• Mixing artificial and natural intelligence: from statistical mechanics to AI and back to turbulence (Q6585765) (← links)
• Koopman dynamic-oriented deep learning for invariant subspace identification and full-state prediction of complex systems (Q6588249) (← links)
• Emergency supplies transportation robot trajectory tracking control based on Koopman and improved event-triggered model predictive control (Q6591162) (← links)
• On higher order drift and diffusion estimates for stochastic SINDy (Q6592244) (← links)
• Phase autoencoder for limit-cycle oscillators (Q6592525) (← links)
• Deep learning in computational mechanics: a review (Q6604128) (← links)
• A LAPACK implementation of the dynamic mode decomposition (Q6604151) (← links)
• Approximation of translation invariant Koopman operators for coupled non-linear systems (Q6604821) (← links)
• Model reduction of dynamical systems with a novel data-driven approach: the RC-HAVOK algorithm (Q6604859) (← links)
• Machine learning in viscoelastic fluids via energy-based kernel embedding (Q6615024) (← links)
• On linear representation, complexity and inversion of maps over finite fields (Q6615540) (← links)
• Data-driven linearization of dynamical systems (Q6617274) (← links)
• Active control of the flow past a circular cylinder using online dynamic mode decomposition (Q6621826) (← links)
• The sparse-grid-based adaptive spectral Koopman method (Q6623708) (← links)
• Representing turbulent statistics with partitions of state space. I: Theory and methodology (Q6629558) (← links)
• Representing turbulent statistics with partitions of state space. II: The compressible Euler equations (Q6629559) (← links)
• Another look at residual dynamic mode decomposition in the regime of fewer snapshots than dictionary size (Q6629751) (← links)
• Approximation identification for the stochastic time-delayed dynamical system (Q6638497) (← links)
• A surrogate reduced order model of the unsteady advection dominant problems based on combination of deep autoencoders-LSTM and POD (Q6645093) (← links)
• Data-driven safe control via finite-time Koopman identifier (Q6645993) (← links)
• Data-driven stabilization of an oscillating flow with linear time-invariant controllers (Q6653311) (← links)
• EDMD for expanding circle maps and their complex perturbations (Q6657422) (← links)
• Informative and non-informative decomposition of turbulent flow fields (Q6659627) (← links)
• System stabilization with policy optimization on unstable latent manifolds (Q6663289) (← links)
• Symbolic extended dynamic mode decomposition (Q6663662) (← links)
• Dictionary-free Koopman model predictive control with nonlinear input transformation (Q6667519) (← links)
• Orthogonal polynomial approximation and extended dynamic mode decomposition in chaos (Q6668645) (← links)