Reduced-Order Modelling for Flow Control

By Bernd R. Noack, Marek Morzynski, Gilead Tadmor. 2013

348 citations recorded by the source corpus.

Subfields: Computational Mechanics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/609ca2612e45ea4a1d466476232ff78f9560555c

How researchers see this paper

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

In Computational Mechanics

1. Steven L. Brunton
2. Eurika Kaiser
3. Jean-Christophe Loiseau
4. Bernd R. Noack
5. Bethany Lusch
6. Thomas Duriez
7. J. Nathan Kutz
8. Sinivsa Krajnovi'c

In Statistical and Nonlinear Physics

1. Steven L. Brunton
2. J. Nathan Kutz
3. Eurika Kaiser
4. Jean-Christophe Loiseau
5. Bethany Lusch
6. Thomas Duriez
7. Bernd R. Noack
8. Earl H. Dowell

Citations in the corpus

• Non-intrusive reduced order modelling of the Navier-Stokes equations (2015, neutral): “Noack [18] and Noori [19] introduced the Neural Network into ROMs.”
• Reduced-order modeling of advection-dominated systems with recurrent neural networks and convolutional autoencoders (2020, background): “As such, this field finds extensive application in control [8, 9, 10, 11], multi-fidelity optimization [12], uncertainty quantification [13, 14] and data-assimilation [15] among others.”
• Low-dimensional modelling of high-Reynolds-number shear flows incorporating constraints from the Navier–Stokes equation (2013, background): “al delity while substantially reducing the size and cost of the computational model. In uid ow applications, Proper Orthogonal Decomposition (POD) and Galerkin projection form a popular MOR strategy (Noack et al. 2011; Holmes et al. 2012). Despite its popularity, deriving robust and accurate ROMs us”
• Cluster-based reduced-order modelling of a mixing layer (2013, methodology): “The evolution equation for the mode coefficient vector a can be derived from a Galerkin projection onto the Navier-Stokes equation (Noack et al. 2011).”
• Constrained sparse Galerkin regression (2016, methodology): “While this may be enforced when the reduced-order model is derived based on a Galerkin projection (Noack et al. 2011; Balajewicz et al. 2013; Carlberg et al. 2015; Schlegel & Noack 2015), these properties need to be actively enforced when a system identification approach such as SINDy is used.”
• Machine Learning Control – Taming Nonlinear Dynamics and Turbulence (2016, methodology): “The associated reduced-order modeling efforts are summarized in these reviews and elaborated in [197, 138].”