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We present applications of modal analysis techniques to study, model, and control canonical aerodynamic flows.
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2002
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2003
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2004
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2004
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2004
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Rowley, C. W., Colonius, T., and Murray, R. M., “Model reduction for compressible flows using POD and Galerkin projection,” Physica D: Nonlinear Phenomena
2004
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2004
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2005
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2006
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2008
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2008
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2009
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2009
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2009
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2009
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2009
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2009
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2009
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2016
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2009
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2009
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Bergmann, M., Bruneaua, C., and Iollo, A., “Enablers for robust POD models,” Journal of Computational Physics
2009
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Schmid, P. J., “Dynamic mode decomposition of numerical and experimental data,” J. Fluid Mech
2010
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2010
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Ahuja, S. and Rowley, C. W., “Feedback control of unstable steady states of flow past a flat plate using reduced-order estimators,” J. Fluid Mech
2010
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Mohren, T. L., Daniel, T. L., Brunton, S. L., and Brunton, B. W., “Neural-inspired sensors enable sparse, efficient classification of spatiotemporal data,” Proceedings of the National Academy of Sciences
2018
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Wan, Z. Y., Vlachas, P., Koumoutsakos, P., and Sapsis, T., “Data-assisted reduced-order modeling of extreme events in complex dynamical systems,” PloS One
2018
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Vlachas, P. R., Byeon, W., Wan, Z. Y., Sapsis, T. P., and Koumoutsakos, P., “Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networks,” Proc. R. Soc. A
2018
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Raissi, M. and Karniadakis, G. E., “Hidden physics models: Machine learning of nonlinear partial differential equations,” Journal of Computational Physics
2018
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2018
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Meena, M. G., Nair, A. G., and Taira, K., “Network community-based model reduction for vortical flows,” Physical Review E
2018
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Korda, M. and Mezić, I., “Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive control,” Automatica
2018
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Kaiser, E., Kutz, J. N., and Brunton, S. L., “Sparse identification of nonlinear dynamics for model predictive control in the low-data limit,” Proceedings of the Royal Society of London A
2018
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2018
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Deem, E., Cattafesta, L., Yao, H., Hemati, M., Zhang, H., and Rowley, C., “Experimental Implementation of Modal Approaches for Autonomous Reattachment of Separated Flows,” AIAA Paper 2018-1052, 2018
2018
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2018
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2018
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Yeh, C.-A. and Taira, K., “Resolvent-analysis-based design of airfoil separation control,” Journal of Fluid Mechanics
2019
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Yao, H. and Hemati, M., “Advances in output feedback control of transient energy growth in a linearized channel flow,” AIAA Paper 2019-0882, 2019
2019
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2019
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Chavarin, A. and Luhar, M., “Resolvent analysis for turbulent channel flow with riblets,” AIAA Journal
2019
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2019
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Ricciardi, T. R., Ribeiro, J. H. M., and Wolf, W. R., “Analysis of coherent structures in large-eddy simulations of a NACA0012 airfoil,” AIAA Paper 2019-0320, 2019
2019
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Benton, S. I. and Visbal, M. R., “The onset of dynamic stall at a high, transitional Reynolds number,” J. Fluid Mech
2019
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Sun, Y., Liu, Q., Cattafesta, L. N., Ukeiley, L. S., and Taira, K., “Effects of sidewalls and leading-edge blowing on flows over long rectangular cavities,” AIAA J
2019
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Zhang, Y., Sun, Y., Arora, N., Cattafesta, L. N., Taira, K., and Ukeiley, L. S., “Suppression of cavity flow oscillations via three-dimensional steady blowing,” AIAA Journal
2019
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Ricciardi, T. R., Wolf, W. R., Kreitzman, J., Moffitt, N. J., and Bent, P., “An assessment of high-fidelity flow simulation methodologies for noise prediction of realistic landing gear configurations,” AIAA Paper 2019-0003, 2019
2019
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2019
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Loiseau, J.-C., Brunton, S. L., and Noack, B. R., “From the POD-Galerkin method to sparse manifold models,” Handbook of Model-Order Reduction. Volume 2: Applications
2019
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2019
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Otto, S. E. and Rowley, C. W., “Linearly recurrent autoencoder networks for learning dynamics,” SIAM Journal on Applied Dynamical Systems
2019
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Brunton, S. L. and Kutz, J. N., Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control
2019
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2019
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Lui, H. F. S. and Wolf, W. R., “Construction of reduced-order models for fluid flows using deep feedforward neural networks,” Journal of Fluid Mechanics
2019
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Raissi, M., Perdikaris, P., and Karniadakis, G., “Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations,” Journal of Computational Physics
2019
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Maulik, R., San, O., Rasheed, A., and Vedula, P., “Subgrid modelling for two-dimensional turbulence using neural networks,” J. Fluid Mech
2019
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Duraisamy, K., Iaccarino, G., and Xiao, H., “Turbulence modeling in the age of data,” Annu. Rev. Fluid Mech
2019
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Nair, A. G., Yeh, C. A., Kaiser, E., Noack, B. R., Brunton, S. L., and Taira, K., “Cluster-based feedback control of turbulent post-stall separated flows,” J. Fluid Mech
2019
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Brunton, S. L., Noack, B. R., and Koumoutsakos, P., “Machine learning in fluid mechanics,” Annual Review of Fluid Mechanics (to appear)
2020
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Brunton, B. W., Brunton, S. L., Proctor, J. L., and Kutz, J. N., “Sparse sensor placement optimization for classification,” SIAM J. App. Math
2099
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