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A review on deep reinforcement learning for fluid mechanics - ScienceDirect
Source: sciencedirect.com
Article summary:
1. This article provides a review of existing applications of deep reinforcement learning (DRL) in the context of fluid dynamics.
2. It compares different approaches and methods used in the considered papers, and discusses combining DRL with computational fluid dynamics (CFD) for flow control and optimization.
3. The paper outlines future possibilities of DRL coupled with fluid dynamics, as well as providing an introduction to modern RL for readers unfamiliar with the topic.
Article analysis:
The article is generally reliable and trustworthy, providing a comprehensive overview of existing applications of deep reinforcement learning (DRL) in the context of fluid dynamics. It presents a thorough review of the literature, covering topics such as choice of DRL algorithm, problem complexity, reward shaping, and more. The article also provides an introduction to modern RL for readers unfamiliar with the topic, which is useful for understanding the concepts discussed throughout the paper.
The article does not appear to be biased or one-sided in its reporting; it presents both sides equally and does not make any unsupported claims or omit any points of consideration. Furthermore, it provides evidence for all claims made throughout the paper and explores counterarguments where necessary. There is no promotional content present in the article either; it is purely informational in nature.
In conclusion, this article is reliable and trustworthy; it provides a comprehensive overview of existing applications of deep reinforcement learning (DRL) in the context of fluid dynamics without bias or one-sidedness.