---
res:
  bibo_abstract:
  - The control of complex systems is of critical importance in many branches of science,
    engineering, and industry, many of which are governed by nonlinear partial differential
    equations. Controlling an unsteady fluid flow is particularly important, as flow
    control is a key enabler for technologies in energy (e.g., wind, tidal, and combustion),
    transportation (e.g., planes, trains, and automobiles), security (e.g., tracking
    airborne contamination), and health (e.g., artificial hearts and artificial respiration).
    However, the high-dimensional, nonlinear, and multi-scale dynamics make real-time
    feedback control infeasible. Fortunately, these high- dimensional systems exhibit
    dominant, low-dimensional patterns of activity that can be exploited for effective
    control in the sense that knowledge of the entire state of a system is not required.
    Advances in machine learning have the potential to revolutionize flow control
    given its ability to extract principled, low-rank feature spaces characterizing
    such complex systems.We present a novel deep learning modelpredictive control
    framework that exploits low-rank features of the flow in order to achieve considerable
    improvements to control performance. Instead of predicting the entire fluid state,
    we use a recurrent neural network (RNN) to accurately predict the control relevant
    quantities of the system, which are then embedded into an MPC framework to construct
    a feedback loop. In order to lower the data requirements and to improve the prediction
    accuracy and thus the control performance, incoming sensor data are used to update
    the RNN online. The results are validated using varying fluid flow examples of
    increasing complexity.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Katharina
      foaf_name: Bieker, Katharina
      foaf_surname: Bieker
      foaf_workInfoHomepage: http://www.librecat.org/personId=32829
  - foaf_Person:
      foaf_givenName: Sebastian
      foaf_name: Peitz, Sebastian
      foaf_surname: Peitz
      foaf_workInfoHomepage: http://www.librecat.org/personId=47427
    orcid: https://orcid.org/0000-0002-3389-793X
  - foaf_Person:
      foaf_givenName: Steven L.
      foaf_name: Brunton, Steven L.
      foaf_surname: Brunton
  - foaf_Person:
      foaf_givenName: J. Nathan
      foaf_name: Kutz, J. Nathan
      foaf_surname: Kutz
  - foaf_Person:
      foaf_givenName: Michael
      foaf_name: Dellnitz, Michael
      foaf_surname: Dellnitz
  bibo_doi: 10.1007/s00162-020-00520-4
  bibo_volume: 34
  dct_date: 2020^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/0935-4964
  - http://id.crossref.org/issn/1432-2250
  dct_language: eng
  dct_title: Deep model predictive flow control with limited sensor data and online
    learning@
...