Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/106379
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Mechanical Engineering | - |
dc.creator | Rabault, J | - |
dc.creator | Ren, F | - |
dc.creator | Zhang, W | - |
dc.creator | Tang, H | - |
dc.creator | Xu, H | - |
dc.date.accessioned | 2024-05-09T00:53:07Z | - |
dc.date.available | 2024-05-09T00:53:07Z | - |
dc.identifier.issn | 1001-6058 | - |
dc.identifier.uri | http://hdl.handle.net/10397/106379 | - |
dc.language.iso | en | en_US |
dc.publisher | Springer | en_US |
dc.rights | ©China Ship Scientific Research Center 2020 | en_US |
dc.rights | This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use(https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms), but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s42241-020-0028-y. | en_US |
dc.subject | Deep reinforcement learning (DRL) | en_US |
dc.subject | Flow control | en_US |
dc.subject | Machine learning | en_US |
dc.title | Deep reinforcement learning in fluid mechanics : a promising method for both active flow control and shape optimization | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 234 | - |
dc.identifier.epage | 246 | - |
dc.identifier.volume | 32 | - |
dc.identifier.issue | 2 | - |
dc.identifier.doi | 10.1007/s42241-020-0028-y | - |
dcterms.abstract | In recent years, artificial neural networks (ANNs) and deep learning have become increasingly popular across a wide range of scientific and technical fields, including fluid mechanics. While it will take time to fully grasp the potentialities as well as the limitations of these methods, evidence is starting to accumulate that point to their potential in helping solve problems for which no theoretically optimal solution method is known. This is particularly true in fluid mechanics, where problems involving optimal control and optimal design are involved. Indeed, such problems are famously difficult to solve effectively with traditional methods due to the combination of non linearity, non convexity, and high dimensionality they involve. By contrast, deep reinforcement learning (DRL), a method of optimization based on teaching empirical strategies to an ANN through trial and error, is well adapted to solving such problems. In this short review, we offer an insight into the current state of the art of the use of DRL within fluid mechanics, focusing on control and optimal design problems. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Journal of hydrodynamics, Apr. 2020, v. 32, no. 2, p. 234-246 | - |
dcterms.isPartOf | Journal of hydrodynamics | - |
dcterms.issued | 2020-04 | - |
dc.identifier.scopus | 2-s2.0-85084357909 | - |
dc.identifier.eissn | 1878-0342 | - |
dc.description.validate | 202405 bcch | - |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | ME-0281 | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | National Numerical Wind Tunnel Project; National Natural Science Foundation of China | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 20685590 | en_US |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Ren_Deep_Reinforcement_Learning.pdf | Pre-Published version | 1.1 MB | Adobe PDF | View/Open |
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