Book,
Dynamic Mode Decomposition : Data-Driven Modeling of Complex Systems
SIAM, (2016)
Abstract
The integration of data and scientific computation is driving a paradigm shift across the engineering, natural, and physical sciences. Indeed, there exists an unprecedented availability of high-fidelity measurements from time-series recordings, numerical simulations, and experimental data. When coupled with readily available algorithms and innovations in machine (statistical) learning, it is possible to extract meaningful spatio-temporal patterns that dominate dynamic activity. The focus of this book is on the emerging method of dynamic mode decomposition (DMD). DMD is a matrix decomposition technique that is highly versatile and builds upon the power of the singular value decomposition (SVD). The low-rank structures extracted from DMD are associated with temporal features as well as correlated spatial activity, thus providing a powerful diagnostic for state estimation, model building, control and prediction.
Tags
- 37m10-time-series-analysis-of-dynamical-systems
- 37n10-dynamical-systems-in-fluid-mechanics-oceanography-fluid-mechanics
- 65f20-overdetermined-systems-pseudoinverses
- 65p99-numerical-problems-in-dynamical-systems
- dblp
- dmd
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Comments and Reviewsshow / hide
- @gdmcbain 4 years ago (last updated 4 years ago)This timely book unfortunately contains a number of grotesque errors: * The Galerkin method does not require an orthogonal basis. (Section 2.1.4) * The eigenvalues of the conjugate transpose of a matrix are the complex conjugates of the eigenvalues of the matrix, regardless of whether the matrix is self-adjoint. (Section 3.1.1) One cannot approve of ch. 4 which heralds 'a transformative improvement in performance that is ideal for video surveillance and recognition applications'.