The following pages link to Applied Koopmanism (Q2944668):
Displaying 50 items.
- Compressed sensing and dynamic mode decomposition (Q317178) (← links)
- A kernel-based method for data-driven Koopman spectral analysis (Q317185) (← links)
- On the numerical approximation of the Perron-Frobenius and Koopman operator (Q333775) (← links)
- Towards tensor-based methods for the numerical approximation of the Perron-Frobenius and Koopman operator (Q523959) (← links)
- Data-driven model reduction and transfer operator approximation (Q722011) (← links)
- Detecting regime transitions in time series using dynamic mode decomposition (Q781788) (← links)
- A data-driven approximation of the koopman operator: extending dynamic mode decomposition (Q897161) (← links)
- Data-driven non-Markovian closure models (Q1656646) (← links)
- Sparsity enabled cluster reduced-order models for control (Q1683852) (← links)
- Greater accuracy and broadened applicability of phase reduction using isostable coordinates (Q1692110) (← links)
- High-dimensional time series prediction using kernel-based koopman mode regression (Q1696900) (← links)
- Mini-workshop: Applied Koopmanism. Abstracts from the mini-workshop held February 7--13, 2016 (Q1698199) (← links)
- Hidden physics models: machine learning of nonlinear partial differential equations (Q1699464) (← links)
- Applied Koopman theory for partial differential equations and data-driven modeling of spatio-temporal systems (Q1723113) (← links)
- Optimal transport over nonlinear systems via infinitesimal generators on graphs (Q1728239) (← links)
- On convergence of extended dynamic mode decomposition to the Koopman operator (Q1744123) (← links)
- Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive control (Q1796994) (← links)
- Comparison of systems with complex behavior (Q1888050) (← links)
- Koopman operator framework for time series modeling and analysis (Q2022703) (← links)
- Koopman operator spectrum for random dynamical systems (Q2022704) (← links)
- On robust computation of Koopman operator and prediction in random dynamical systems (Q2022706) (← links)
- Spectrum of the Koopman operator, spectral expansions in functional spaces, and state-space geometry (Q2022707) (← links)
- Data-driven Koopman operator approach for computational neuroscience (Q2023876) (← links)
- Geometric considerations of a good dictionary for Koopman analysis of dynamical systems: cardinality, ``primary eigenfunction,'' and efficient representation (Q2025462) (← links)
- Koopman operator approach for computing structure of solutions and observability of nonlinear dynamical systems over finite fields (Q2036487) (← links)
- Reproducing kernel Hilbert space compactification of unitary evolution groups (Q2036491) (← links)
- Dynamic mode decomposition for continuous time systems with the Liouville operator (Q2062874) (← links)
- Low-rank dynamic mode decomposition: an exact and tractable solution (Q2062877) (← links)
- Existence and uniqueness of global Koopman eigenfunctions for stable fixed points and periodic orbits (Q2077724) (← links)
- Data-driven operator theoretic methods for phase space learning and analysis (Q2083237) (← links)
- Koopman wavefunctions and classical states in hybrid quantum-classical dynamics (Q2086016) (← links)
- Koopman-based spectral clustering of directed and time-evolving graphs (Q2096996) (← links)
- Stabilized neural ordinary differential equations for long-time forecasting of dynamical systems (Q2112549) (← links)
- Data-driven approximation of the Koopman generator: model reduction, system identification, and control (Q2115518) (← links)
- Operator-theoretic framework for forecasting nonlinear time series with kernel analog techniques (Q2125604) (← links)
- System identification through Lipschitz regularized deep neural networks (Q2132640) (← links)
- A Koopman framework for rare event simulation in stochastic differential equations (Q2133784) (← links)
- Correcting noisy dynamic mode decomposition with Kalman filters (Q2137999) (← links)
- Network resilience (Q2144481) (← links)
- Quantitative comparison of the mean-return-time phase and the stochastic asymptotic phase for noisy oscillators (Q2145423) (← links)
- tgEDMD: approximation of the Kolmogorov operator in tensor train format (Q2146443) (← links)
- Model and data reduction for data assimilation: particle filters employing projected forecasts and data with application to a shallow water model (Q2147287) (← links)
- Solving eigenvalue PDEs of metastable diffusion processes using artificial neural networks (Q2157080) (← links)
- A preconditioning technique for Krylov subspace methods in RKHSs (Q2161048) (← links)
- Relatively uniformly continuous semigroups on vector lattices (Q2188329) (← links)
- A tale of two vortices: how numerical ergodic theory and transfer operators reveal fundamental changes to coherent structures in non-autonomous dynamical systems (Q2194428) (← links)
- Dynamic mode decomposition for analytic maps (Q2204447) (← links)
- Dynamics reconstruction and classification via Koopman features (Q2218387) (← links)
- Extraction and prediction of coherent patterns in incompressible flows through space-time koopman analysis (Q2222732) (← links)
- Krylov subspace methods for estimating operator-vector multiplications in Hilbert spaces (Q2231599) (← links)
