M2P 2023

Optimal Control tools to minimize dispersion in turbulent flows

  • Calascibetta, Chiara (University of Rome Tor Vergata and INFN)
  • Biferale, Luca (University of Rome Tor Vergata and INFN)
  • Borra, Francesco (Laboratory of Physics of the Ecole Normale Su)
  • Celani, Antonio (Quantitative Life Sciences, the Abdus Salam I)
  • Cencini, Massimo (Istituto dei Sistemi Complessi, CNR and INFN)

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We develop optimal and quasi-optimal strategies to control Lagrangian objects navigating in 3d turbulent flows. We consider the problem of minimizing the dispersion rate of a couple of autonomous explorers moving into the complex fluid environment. Starting from the optimal solutions derived in control theory, we find approximated solutions that could be applied also under less restrictive conditions as, e.g., in the presence of partial observability. We are going to compare hard-wired policies resulting from different approximated solutions of the optimal control theory against strategies obtained by data-driven tools based on Reinforcement Learning. This work was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant Agreement No. 882340).