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Award ID contains: 2052811

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  1. ; ; (, Journal of Dynamic Systems, Measurement, and Control)
    Abstract In this work, we consider the problem of learning a reduced-order model of a high-dimensional stochastic nonlinear system with control inputs from noisy data. In particular, we develop a hybrid parametric/nonparametric model that learns the “average” linear dynamics in the data using dynamic mode decomposition with control (DMDc) and the nonlinearities and model uncertainties using Gaussian process (GP) regression and compare it with total least-squares dynamic mode decomposition (tlsDMD), extended here to systems with control inputs (tlsDMDc). The proposed approach is also compared with existing methods, such as DMDc-only and GP-only models, in two tasks: controlling the stochastic nonlinear Stuart–Landau equation and predicting the flowfield induced by a jet-like body force field in a turbulent boundary layer using data from large-scale numerical simulations. 
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    Full Text Available 
  2. ; ; ; ; (, AIAA)
    Free, publicly-accessible full text available January 10, 2026
  3. ; ; ; ; (, American Institute of Aeronautics and Astronautics)
    Free, publicly-accessible full text available January 3, 2026
  4. ; ; ; (, American Institute of Aeronautics and Astronautics)
    Free, publicly-accessible full text available January 3, 2026
  5. ; ; (, ASME Journal of Dynamic Systems, Measurement and Control)
    In this work, we consider the problem of learning a reduced-order model of a high-dimensional stochastic nonlinear system with control inputs from noisy data. In particular, we develop a hybrid parametric/nonparametric model that learns the “average” linear dynamics in the data using dynamic mode decomposition with control (DMDc) and the nonlinearities and model uncertainties using Gaussian process (GP) regression and compare it with total least-squares dynamic mode decomposition (tlsDMD), extended here to systems with control inputs (tlsDMDc). The proposed approach is also compared with existing methods, such as DMDconly and GP-only models, in two tasks: controlling the stochastic nonlinear Stuart–Landau equation and predicting the flowfield induced by a jet-like body force field in a turbulent boundary layer using data from large-scale numerical simulations. 
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    Free, publicly-accessible full text available November 20, 2025
  6. ; ; ; (, American Institute of Aeronautics and Astronautics)
    Full Text Available 
  7. ; (, IEEE)
    Full Text Available 
  8. ; ; ; ; ; ; (, TSFP)
    Accepted Manuscript Full Text Available
  9. ; ; ; ; ; ; (, American Institute of Aeronautics and Astronautics)
  10. ; ; ; ; (, Physics of Fluids)
    Turbulent boundary layers are dominated by large-scale motions (LSMs) of streamwise momentum surplus and deficit that contribute significantly to the statistics of the flow. In particular, the high-momentum LSMs residing in the outer region of the boundary layer have the potential to re-energize the flow and delay separation if brought closer to the wall. This work explores the effect of selectively manipulating LSMs in a moderate Reynolds number turbulent boundary layer for separation delay via well-resolved large-eddy simulations. Toward that goal, a model predictive control scheme is developed based on a reduced-order model of the flow that directs LSMs of interest closer to the wall in an optimal way via a body force-induced downwash. The performance improvement achieved by targeting LSMs for separation delay, compared to a naive actuation scheme that does not account for the presence of LSMs, is demonstrated. 
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