Nonlinear Model Order Reduction via Dynamic Mode Decomposition
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Publication:5357971
DOI10.1137/16M1059308zbMath1373.65090arXiv1602.05080OpenAlexW2963167725MaRDI QIDQ5357971
Alessandro Alla, J. Nathan Kutz
Publication date: 18 September 2017
Published in: SIAM Journal on Scientific Computing (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1602.05080
numerical examplesproper orthogonal decompositionnonlinear dynamical systemsdimensionality reductionreduced order modelingdynamic mode decompositiondata-driven modeling
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Cites Work
- Unnamed Item
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- Compressed sensing and dynamic mode decomposition
- Reduced order methods for modeling and computational reduction
- The GNAT method for nonlinear model reduction: effective implementation and application to computational fluid dynamics and turbulent flows
- An `empirical interpolation' method: Application to efficient reduced-basis discretization of partial differential equations
- Comparison of systems with complex behavior
- Spectral properties of dynamical systems, model reduction and decompositions
- Unsteady flow sensing and estimation via the gappy proper orthogonal decomposition
- On dynamic mode decomposition: theory and applications
- A New Selection Operator for the Discrete Empirical Interpolation Method---Improved A Priori Error Bound and Extensions
- Multiresolution Dynamic Mode Decomposition
- A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems
- Sparse Sensor Placement Optimization for Classification
- Online Adaptive Model Reduction for Nonlinear Systems via Low-Rank Updates
- The Optimal Hard Threshold for Singular Values is <inline-formula> <tex-math notation="TeX">\(4/\sqrt {3}\) </tex-math></inline-formula>
- Nonlinear Model Reduction via Discrete Empirical Interpolation
- Dynamic mode decomposition of numerical and experimental data
- Spectral analysis of nonlinear flows
- A ‘best points’ interpolation method for efficient approximation of parametrized functions
- Turbulence and the dynamics of coherent structures. I. Coherent structures
- Hamiltonian Systems and Transformation in Hilbert Space
- Galerkin Proper Orthogonal Decomposition Methods for a General Equation in Fluid Dynamics
- Analysis of Fluid Flows via Spectral Properties of the Koopman Operator
- Nonlinear Model Order Reduction via Dynamic Mode Decomposition
- Turbulence, Coherent Structures, Dynamical Systems and Symmetry
- Localized Discrete Empirical Interpolation Method
- Galerkin proper orthogonal decomposition methods for parabolic problems