Model Reduction for Flow Analysis and Control

By Clarence W. Rowley, Scott T. M. Dawson. 2017

776 citations recorded by the source corpus.

Subfields: Computational Mechanics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/4d3a2375f171c643c3ef9a133f5deabaf8b19d4e

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

In Statistical and Nonlinear Physics

Citations in the corpus

Deep Dynamical Modeling and Control of Unsteady Fluid Flows (2018, methodology): “This research has yielded a wide array of techniques for learning data-driven dynamical models, including balanced truncation, proper orthogonal decomposition, and dynamic mode decomposition [12].”
Constrained sparse Galerkin regression (2016, methodology): “Over the past decades, identifying robust, accurate and efficient reduced-order models has thus become a central challenge in fluid dynamics and closed-loop flow control (Brunton and Noack, 2015; Fabbiane et al., 2014; Rowley and Dawson, 2016; Sipp and Schmid, 2016).”
Nonlinear mode decomposition with convolutional neural networks for fluid dynamics (2019, background): “…possible to construct control laws based on reduced-order models at low computational cost (Bergmann, Cordier & Brancher 2005; Samimy et al. 2007; Rowley & Dawson 2017), since the time-evolving ﬂow ﬁeld can be represented by a linear combination of the expansion coefﬁcients and the orthogonal…”
Multi-scale proper orthogonal decomposition of complex fluid flows (2018, methodology): “Recent reviews on data-driven decomposition with emphasis on fluid dynamics are proposed by Taira et al. (2017) and Rowley & Dawson (2017), while an extensive overview on the impact of the big data revolution in turbulence research is presented by Pollard et al. (2017), while Duriez et al. (2017)…”
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.”
Modal Analysis of Fluid Flows: An Overview (2017, neutral): “For further details, refer to the work of Rowley and Dawson [2], Tu et al.”
Digital Twin: Values, Challenges and Enablers From a Modeling Perspective (2019, neutral): “[260] C.”
Coherent structures in wall-bounded turbulence (2017, methodology): “Reduced-order models seek to project the equations of motion onto a smaller set of variables that approximate the solution in some global sense, typically a linear subspace or a few Fourier modes (see, for example, Rowley & Dawson 2017).”
Linearly-Recurrent Autoencoder Networks for Learning Dynamics (2017, neutral): “This method is sometimes called overspecification [24] and essentially determines an encoder function into an appropriate small set of features.”
Machine Learning for Fluid Mechanics (2019, background): “DMD is closely related to the Koopman operator (Rowley et al. 2009; Mezic 2013), which is an inﬁnite dimensional linear operator that describes how all measurement functions of the system evolve in time.”
Spectral proper orthogonal decomposition and its relationship to dynamic mode decomposition and resolvent analysis (2017, methodology): “An efficient algorithm for computing space-only POD modes from snapshots of discrete flow data using the method of snapshots (Sirovich, 1987) is well-known and described in detail in numerous publications (e.g., Rowley & Dawson, 2017; Taira et al., 2017).”