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 50 items.

• Data‐driven identification of the spatiotemporal structure of turbulent flows by streaming dynamic mode decomposition (Q6089589) (← links)
• Deep Koopman model predictive control for enhancing transient stability in power grids (Q6089850) (← links)
• CD-ROM: complemented deep -- reduced order model (Q6094649) (← links)
• Human-centered driving authority allocation for driver-automation shared control: a two-layer game-theoretic approach (Q6095665) (← links)
• Combining dynamic mode decomposition with ensemble Kalman filtering for tracking and forecasting (Q6098248) (← links)
• Predicting rare events using neural networks and short-trajectory data (Q6104999) (← links)
• The mpEDMD Algorithm for Data-Driven Computations of Measure-Preserving Dynamical Systems (Q6108137) (← links)
• Generalizing dynamic mode decomposition: balancing accuracy and expressiveness in Koopman approximations (Q6110261) (← links)
• A real-time identification method of network structure in complex network systems (Q6111213) (← links)
• Active Operator Inference for Learning Low-Dimensional Dynamical-System Models from Noisy Data (Q6113944) (← links)
• Parsimony as the ultimate regularizer for physics-informed machine learning (Q6117148) (← links)
• Data‐assisted control: A framework development by exploiting NASA Generic Transport platform (Q6117642) (← links)
• Constrained optimized dynamic mode decomposition with control for physically stable systems with exogeneous inputs (Q6117704) (← links)
• Probabilistic forecast of nonlinear dynamical systems with uncertainty quantification (Q6117709) (← links)
• Data-driven inference of low order representations of observable dynamics for an airfoil model (Q6117712) (← links)
• Koopman operator learning using invertible neural networks (Q6126575) (← links)
• Operator inference with roll outs for learning reduced models from scarce and low-quality data (Q6135185) (← links)
• Quantum Mechanics for Closure of Dynamical Systems (Q6150480) (← links)
• Stochastic dynamics and data science (Q6151506) (← links)
• Data-driven method to extract mean exit time and escape probability for dynamical systems driven by Lévy noises (Q6151512) (← links)
• Reduced Order Characterization of Nonlinear Oscillations Using an Adaptive Phase-Amplitude Coordinate Framework (Q6151658) (← links)
• Data driven discovery of systems of ordinary differential equations using nonconvex multitask learning (Q6161211) (← links)
• Mean resolvent operator of a statistically steady flow (Q6166727) (← 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)
• 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)
• Data-driven model identification using forcing-induced limit cycles (Q6191513) (← links)
• Ensemble forecasts in reproducing kernel Hilbert space family (Q6191535) (← links)
• Piecewise DMD for oscillatory and Turing spatio-temporal dynamics (Q6202634) (← 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)
• A Koopman-Takens theorem: linear least squares prediction of nonlinear time series (Q6536643) (← links)
• An adaptive method based on local dynamic mode decomposition for parametric dynamical systems (Q6537069) (← links)
• Deep Koopman learning of nonlinear time-varying systems (Q6537328) (← links)
• Machine learning enhanced Hankel dynamic-mode decomposition (Q6550749) (← links)
• Symbolic regression via neural networks (Q6550761) (← links)
• Koopman modeling and optimal control for microbial fed-batch fermentation with switching operators (Q6551640) (← 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)
• Learning nonparametric ordinary differential equations from noisy data (Q6553789) (← links)
• Data-driven models of nonautonomous systems (Q6553794) (← links)
• Enhancing predictive capabilities in data-driven dynamical modeling with automatic differentiation: Koopman and neural ODE approaches (Q6554429) (← links)
• Generalized quadratic embeddings for nonlinear dynamics using deep learning (Q6554923) (← links)
• Propagating uncertainty through system dynamics in reproducing kernel Hilbert space (Q6554937) (← links)
• The occupation kernel method for nonlinear system identification (Q6555693) (← links)
• Randomized dynamic mode decomposition for nonintrusive reduced order modelling (Q6557432) (← links)
• Deep learning enhanced dynamic mode decomposition (Q6560590) (← links)