Machine Learning Control – Taming Nonlinear Dynamics and Turbulence

By Bernd R. Noack, Thomas Duriez, Steven L. Brunton. Fluid Mechanics and Its Applications, 2016

302 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/9646c61e9cbf3dd1fadc9e03d5581eaa34da7047

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In Artificial Intelligence

In Control and Systems Engineering

Citations in the corpus

Data-driven discovery of Koopman eigenfunctions for control (2017, background): “A wide range of data-driven and nonlinear control approaches exist in the literature, including model-free adaptive control [12]–[14], extremum-seeking [15]–[17], gain scheduling [18], [19], feedback linearization [20]–[22], describing functions [23], sliding mode control [24]–[26], singular perturb”
Model selection for dynamical systems via sparse regression and information criteria (2017, methodology): “Genetic programming has been recently used to determine the structure and parameters of dynamical systems [23, 17, 24] and control laws [25], enabling the eﬃcient search of complex function spaces.”
Artificial neural networks trained through deep reinforcement learning discover control strategies for active flow control (2018, background): “07 66 4v 4 [ ph ys ic s. fl Unfortunately, designing active flow control strategies is a complex endeavor (Duriez et al., 2016).”
Data-Driven Sparse Sensor Placement for Reconstruction: Demonstrating the Benefits of Exploiting Known Patterns (2017, methodology): “Recently, compressed sensing, sparsity-promoting algorithms such as the lasso regression [35], [36], [37], and machine learning have been increasingly applied to characterize and control dynamical systems [38], [39], [40], [41], [42], [43], [44], [45], [46], [47].”
Machine Learning for Fluid Mechanics (2019, methodology): “We refer to Duriez et al. (2016) for an in-depth description of the method and to Noack (2018) for an overview of the plants.”
Adaptive separation control of a laminar boundary layer using online dynamic mode decomposition (2020, methodology): “More recently, methods from machine learning have been employed in flow control, in which the control design is framed as a regression problem and solved by genetic algorithms without explicit knowledge of the dynamics (Duriez et al. 2017; Li et al. 2017; Wu 2018).”
Multi-scale proper orthogonal decomposition of complex fluid flows (2018, background): “…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) give an overview of machine learning methods for flow control.”