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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2019
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/1908.04127 |
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| _version_ | 1866916290871951360 |
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| author | Garnier, Paul Viquerat, Jonathan Rabault, Jean Larcher, Aurélien Kuhnle, Alexander Hachem, Elie |
| author_facet | Garnier, Paul Viquerat, Jonathan Rabault, Jean Larcher, Aurélien Kuhnle, Alexander Hachem, Elie |
| contents | 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 applications to fluid mechanics problems. In addition, we present recent results that further illustrate the potential of DRL in Fluid Mechanics. The coupling methods used in each case are covered, detailing their advantages and limitations. Our review also focuses on the comparison with classical methods for optimal control and optimization. Finally, several test cases are described that illustrate recent progress made in this field. The goal of this publication is to provide an understanding of DRL capabilities along with state-of-the-art applications in fluid dynamics to researchers wishing to address new problems with these methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1908_04127 |
| institution | arXiv |
| publishDate | 2019 |
| record_format | arxiv |
| spellingShingle | A review on Deep Reinforcement Learning for Fluid Mechanics Garnier, Paul Viquerat, Jonathan Rabault, Jean Larcher, Aurélien Kuhnle, Alexander Hachem, Elie Computational Physics Machine Learning Fluid Dynamics 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 applications to fluid mechanics problems. In addition, we present recent results that further illustrate the potential of DRL in Fluid Mechanics. The coupling methods used in each case are covered, detailing their advantages and limitations. Our review also focuses on the comparison with classical methods for optimal control and optimization. Finally, several test cases are described that illustrate recent progress made in this field. The goal of this publication is to provide an understanding of DRL capabilities along with state-of-the-art applications in fluid dynamics to researchers wishing to address new problems with these methods. |
| title | A review on Deep Reinforcement Learning for Fluid Mechanics |
| topic | Computational Physics Machine Learning Fluid Dynamics |
| url | https://arxiv.org/abs/1908.04127 |