Low-Dimensional Approach for Reconstruction of Airfoil Data via Compressive Sensing

By Zhe Bai, Thakshila Wimalajeewa, Z. Berger, Guannan Wang, M. Glauser, Pramod K. Varshney. 2015

76 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computational Mechanics, Statistical and Nonlinear Physics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. J. Nathan Kutz
2. Joshua L. Proctor
3. Bingni W. Brunton
4. Krithika Manohar
5. Shervin Bagheri
6. Scott T. M. Dawson
7. Maizar Raissi
8. Karthik Duraisamy

In Computational Mechanics

1. Steven L. Brunton
2. J. Nathan Kutz
3. Joshua L. Proctor
4. Scott T. M. Dawson
5. Bingni W. Brunton
6. Maizar Raissi
7. Krithika Manohar
8. Maziar S. Hemati

In Statistical and Nonlinear Physics

1. Steven L. Brunton
2. J. Nathan Kutz
3. Maizar Raissi
4. Joshua L. Proctor
5. Bingni W. Brunton
6. G. Em Karniadakis
7. Scott T. M. Dawson
8. Krithika Manohar

Citations in the corpus

• Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations (2018, methodology): “More recently, sparsity (Tibshirani, 1996) has been used to determine the governing dynamical system (Brunton et al., 2016; Mangan et al., 2016; Wang et al., 2011; Schaeffer et al., 2013; Ozoliņš et al., 2013; Mackey et al., 2014; Brunton et al., 2014; Proctor et al., 2014; Bai et al., 2014; Tran an”
• Discovering governing equations from data by sparse identification of nonlinear dynamical systems (2015, background): “The trend to exploit sparsity in dynamical systems is recent but growing [24, 25, 26, 27, 28, 29, 30].”
• Data-Driven Sparse Sensor Placement for Reconstruction: Demonstrating the Benefits of Exploiting Known Patterns (2017, methodology): “These techniques have been effective in modeling high-dimensional fluid systems using POD [48] and DMD [49], [50], [51], [52].”
• Modal Analysis of Fluid Flows: Applications and Outlook (2019, background): “ements [201,202] and fast randomized computations [203]. Compressed sensing has already been leveraged for compact representations of wall-bounded turbulence [204] and for POD based ow reconstruction [205]. If only classication or detection is required, reconstruction can be circumvented and the mea”
• Koopman Invariant Subspaces and Finite Linear Representations of Nonlinear Dynamical Systems for Control (2015, background): “This follows a growing trend to exploit sparsity in dynamics [35, 36, 37] and dynamical systems [38, 39, 40].”