Sparse identification of nonlinear dynamics with low-dimensionalized flow representations
From MaRDI portal
Publication:5154682
DOI10.1017/jfm.2021.697zbMath1500.76068arXiv2010.12177OpenAlexW3196966183MaRDI QIDQ5154682
Kai Fukami, Takaaki Murata, Koji Fukagata, Kai Zhang
Publication date: 5 October 2021
Published in: Journal of Fluid Mechanics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2010.12177
machine learningadaptive least absolute shrinkage/selection operatorconvolutional neural network autoencodersingle cylinder wakethresholded least square algorithmtwo-parallel cylinders wake
Learning and adaptive systems in artificial intelligence (68T05) Wakes and jets (76D25) Basic methods in fluid mechanics (76M99)
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Uses Software
Cites Work
- Discovering governing equations from data by sparse identification of nonlinear dynamical systems
- The Adaptive Lasso and Its Oracle Properties
- Neural network modeling for near wall turbulent flow.
- Multi-level convolutional autoencoder networks for parametric prediction of spatio-temporal dynamics
- Recovering missing CFD data for high-order discretizations using deep neural networks and dynamics learning
- Reducing the Dimensionality of Data with Neural Networks
- Characteristics of flow over two circular cylinders in a side-by-side arrangement at low Reynolds numbers
- Sparse reduced-order modelling: sensor-based dynamics to full-state estimation
- Spectral proper orthogonal decomposition and its relationship to dynamic mode decomposition and resolvent analysis
- Deterministic Nonperiodic Flow
- Data-Driven Science and Engineering
- 9 From the POD-Galerkin method to sparse manifold models
- Spectral proper orthogonal decomposition and resolvent analysis of near-wall coherent structures in turbulent pipe flows
- Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows
- On the Convergence of the SINDy Algorithm
- Low-order model for successive bifurcations of the fluidic pinball
- Data-driven discovery of coordinates and governing equations
- Constrained sparse Galerkin regression
- Nonlinear mode decomposition with convolutional neural networks for fluid dynamics
- Sparse identification of nonlinear dynamics for model predictive control in the low-data limit
- Regularization and Variable Selection Via the Elastic Net
- Discovery of Nonlinear Multiscale Systems: Sampling Strategies and Embeddings
- Global stability of base and mean flows: a general approach and its applications to cylinder and open cavity flows
- Ridge Regression: Biased Estimation for Nonorthogonal Problems
- Periodic Orbits and Chaotic Sets in a Low-Dimensional Model for Shear Flows
- Sparse identification of multiphase turbulence closures for coupled fluid–particle flows
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