@article{KaiserNoackCordieretal.2014,
  author    = {Kaiser, Eurika and Noack, Bernd R. and Cordier, Laurent and Spohn, Andreas and Segond, Marc and Abel, Markus and Daviller, Guillaume and Osth, Jan and Krajnovic, Sinisa and Niven, Robert K.},
  title     = {Cluster-based reduced-order modelling of a mixing layer},
  series = {Journal of fluid mechanics},
  volume    = {754},
  journal   = {Journal of fluid mechanics},
  publisher = {Cambridge Univ. Press},
  address   = {New York},
  issn      = {0022-1120},
  doi       = {10.1017/jfm.2014.355},
  pages     = {365 -- 414},
  year      = {2014},
  language  = {en}
}
@article{ParezanovicCordierSpohnetal.2016,
  author    = {Parezanovic, Vladimir and Cordier, Laurent and Spohn, Andreas and Duriez, Thomas and Noack, Bernd R. and Bonnet, Jean-Paul and Segond, Marc and Abel, Markus and Brunton, Steven L.},
  title     = {Frequency selection by feedback control in a turbulent shear flow},
  series = {Journal of fluid mechanics},
  volume    = {797},
  journal   = {Journal of fluid mechanics},
  publisher = {Cambridge Univ. Press},
  address   = {New York},
  issn      = {0022-1120},
  doi       = {10.1017/jfm.2016.261},
  pages     = {247 -- 283},
  year      = {2016},
  abstract  = {Many previous studies have shown that the turbulent mixing layer under periodic forcing tends to adopt a lock-on state, where the major portion of the fluctuations in the flow are synchronized at the forcing frequency. The goal of this experimental study is to apply closed-loop control in order to provoke the lock-on state, using information from the flow itself. We aim to determine the range of frequencies for which the closed-loop control can establish the lock-on, and what mechanisms are contributing to the selection of a feedback frequency. In order to expand the solution space for optimal closed-loop control laws, we use the genetic programming control (CPC) framework. The best closed-loop control laws obtained by CPC are analysed along with the associated physical mechanisms in the mixing layer flow. The resulting closed-loop control significantly outperforms open-loop forcing in terms of robustness to changes in the free-stream velocities. In addition, the selection of feedback frequencies is not locked to the most amplified local mode, but rather a range of frequencies around it.},
  language  = {en}
}
@article{ParezanovicLaurentieFourmentetal.2015,
  author    = {Parezanovic, Vladimir and Laurentie, Jean-Charles and Fourment, Carine and Delville, Joel and Bonnet, Jean-Paul and Spohn, Andreas and Duriez, Thomas and Cordier, Laurent and Noack, Bernd R. and Abel, Markus and Segond, Marc and Shaqarin, Tamir and Brunton, Steven L.},
  title     = {Mixing layer manipulation experiment from open-loop forcing to closed-loop machine learning control},
  series = {Flow, turbulence and combustion : an international journal published in association with ERCOFTAC},
  volume    = {94},
  journal   = {Flow, turbulence and combustion : an international journal published in association with ERCOFTAC},
  number    = {1},
  publisher = {Springer},
  address   = {Dordrecht},
  issn      = {1386-6184},
  doi       = {10.1007/s10494-014-9581-1},
  pages     = {155 -- 173},
  year      = {2015},
  language  = {en}
}
@article{QuadeAbelShafietal.2016,
  author    = {Quade, Markus and Abel, Markus and Shafi, Kamran and Niven, Robert K. and Noack, Bernd R.},
  title     = {Prediction of dynamical systems by symbolic regression},
  series = {Physical review : E, Statistical, nonlinear and soft matter physics},
  volume    = {94},
  journal   = {Physical review : E, Statistical, nonlinear and soft matter physics},
  publisher = {American Society for Pharmacology and Experimental Therapeutics},
  address   = {Bethesda},
  issn      = {2470-0045},
  doi       = {10.1103/PhysRevE.94.012214},
  pages     = {15},
  year      = {2016},
  abstract  = {We study the modeling and prediction of dynamical systems based on conventional models derived from measurements. Such algorithms are highly desirable in situations where the underlying dynamics are hard to model from physical principles or simplified models need to be found. We focus on symbolic regression methods as a part of machine learning. These algorithms are capable of learning an analytically tractable model from data, a highly valuable property. Symbolic regression methods can be considered as generalized regression methods. We investigate two particular algorithms, the so-called fast function extraction which is a generalized linear regression algorithm, and genetic programming which is a very general method. Both are able to combine functions in a certain way such that a good model for the prediction of the temporal evolution of a dynamical system can be identified. We illustrate the algorithms by finding a prediction for the evolution of a harmonic oscillator based on measurements, by detecting an arriving front in an excitable system, and as a real-world application, the prediction of solar power production based on energy production observations at a given site together with the weather forecast.},
  language  = {en}
}