Corpus ID: 199543817

A review on Deep Reinforcement Learning for Fluid Mechanics

@article{Garnier2019ARO,
  title={A review on Deep Reinforcement Learning for Fluid Mechanics},
  author={Paul Garnier and Jonathan Viquerat and Jean Rabault and Aur'elien Larcher and Alexander Kuhnle and Elie Hachem},
  journal={ArXiv},
  year={2019},
  volume={abs/1908.04127}
}
  • Paul Garnier, Jonathan Viquerat, +3 authors Elie Hachem
  • Published in ArXiv 2019
  • Mathematics, Physics, Computer Science
  • Deep reinforcement learning (DRL) has recently been adopted in a wide range of physics and engineering domains for its ability to solve decision-making problems that were previously out of reach due to a combination of non-linearity and high dimensionality. In the last few years, it has spread in the field of computational mechanics, and particularly in fluid dynamics, with recent applications in flow control and shape optimization. In this work, we conduct a detailed review of existing DRL… CONTINUE READING

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