Pages that link to "Item:Q4907902"

The following pages link to Analysis of Fluid Flows via Spectral Properties of the Koopman Operator (Q4907902):

Displaying 43 items.

• Symmetry-reduced low-dimensional representation of large-scale dynamics in the asymptotic suction boundary layer (Q6078467) (← links)
• Deep Koopman model predictive control for enhancing transient stability in power grids (Q6089850) (← links)
• CD-ROM: complemented deep -- reduced order model (Q6094649) (← links)
• Characterization of energy transfer and triadic interactions of coherent structures in turbulent wakes (Q6096865) (← links)
• A study on data-driven identification and representation of nonlinear dynamical systems with a physics-integrated deep learning approach: Koopman operators and nonlinear normal modes (Q6105292) (← 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)
• Data-driven inference of low order representations of observable dynamics for an airfoil model (Q6117712) (← links)
• Phase-amplitude coordinate-based neural networks for inferring oscillatory dynamics (Q6123359) (← links)
• Reduced Order Characterization of Nonlinear Oscillations Using an Adaptive Phase-Amplitude Coordinate Framework (Q6151658) (← links)
• Mean resolvent operator of a statistically steady flow (Q6166727) (← links)
• A Reduced Order Modeling Framework for Strongly Perturbed Nonlinear Dynamical Systems Near Arbitrary Trajectory Sets (Q6168207) (← links)
• Koopman Operator Inspired Nonlinear System Identification (Q6171206) (← links)
• Rigorous data‐driven computation of spectral properties of Koopman operators for dynamical systems (Q6180710) (← links)
• Dynamic mode decomposition: an alternative algorithm for full-rank datasets (Q6185526) (← links)
• Data-driven model identification using forcing-induced limit cycles (Q6191513) (← links)
• Spectral proper orthogonal decomposition of harmonically forced turbulent flows (Q6496919) (← links)
• The identification of piecewise non-linear dynamical system without understanding the mechanism (Q6548691) (← links)
• Koopman analysis of the periodic Korteweg-de Vries equation (Q6551381) (← links)
• Learning physics-based reduced-order models from data using nonlinear manifolds (Q6552123) (← links)
• Machine discovery of partial differential equations from spatiotemporal data: a sparse Bayesian learning framework (Q6553198) (← links)
• Model reduction for nonlinear multiscale parabolic problems using dynamic mode decomposition (Q6553505) (← links)
• Enhancing predictive capabilities in data-driven dynamical modeling with automatic differentiation: Koopman and neural ODE approaches (Q6554429) (← links)
• Fast optimal entrainment of limit-cycle oscillators by strong periodic inputs via phase-amplitude reduction and Floquet theory (Q6556969) (← links)
• Randomized dynamic mode decomposition for nonintrusive reduced order modelling (Q6557432) (← links)
• Data-driven identification of dynamical models using adaptive parameter sets (Q6561192) (← links)
• Phase space partition with Koopman analysis (Q6565174) (← 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)
• On principles of emergent organization (Q6571624) (← links)
• Piecewise integrable neural network: an interpretable chaos identification framework (Q6572660) (← links)
• Koopman dynamic-oriented deep learning for invariant subspace identification and full-state prediction of complex systems (Q6588249) (← links)
• Model adaptive phase space reconstruction (Q6592610) (← links)
• A LAPACK implementation of the dynamic mode decomposition (Q6604151) (← 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)
• SDYN-GANs: adversarial learning methods for multistep generative models for general order stochastic dynamics (Q6639347) (← links)
• Wavelet-based resolvent analysis of non-stationary flows (Q6653296) (← links)
• Characterizing nonlinear flow dynamics in hydrodynamic and magnetohydrodynamic regimes through modal decomposition (Q6658626) (← links)
• Informative and non-informative decomposition of turbulent flow fields (Q6659627) (← links)
• Koopman analysis of the singularly perturbed van der Pol oscillator (Q6663652) (← links)
• Interpretable a-posteriori error indication for graph neural network surrogate models (Q6669010) (← links)