Perspective on machine learning for advancing fluid mechanics

Perspective on machine learning for advancing fluid mechanics

In this paper, a perspective is presented on how machine learning (ML), with its burgeoning popularity and the increasing availability of portable implementations, might advance fluid mechanics. As with any numerical or experimental method, ML methods have strengths and limitations, which are acknow...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Physical review fluids 2019-10, Vol.4 (10), Article 100501
Hauptverfasser: Brenner, M. P., Eldredge, J. D., Freund, J. B.
Format: Artikel
Sprache:eng
Schlagworte:
ENGINEERING
Physics
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue 10
container_start_page
container_title Physical review fluids
container_volume 4
creator Brenner, M. P.
Eldredge, J. D.
Freund, J. B.
description In this paper, a perspective is presented on how machine learning (ML), with its burgeoning popularity and the increasing availability of portable implementations, might advance fluid mechanics. As with any numerical or experimental method, ML methods have strengths and limitations, which are acknowledged. Their potential impact is high so long as outcomes are held to the long-standing critical standards that should guide studies of flow physics.
doi_str_mv 10.1103/PhysRevFluids.4.100501
format Article
fullrecord <record><control><sourceid>crossref_osti_</sourceid><recordid>TN_cdi_osti_scitechconnect_1801081</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>10_1103_PhysRevFluids_4_100501</sourcerecordid><originalsourceid>FETCH-LOGICAL-c449t-310e73495e4ea17b7a8bda01bf0d0cc36e83b97c947fb0d2adf9b0803017dbc43</originalsourceid><addsrcrecordid>eNpVkE9LAzEQxYMoWGq_gizet8406WZz8CDFWqFgEQVvIX9mbaTNlmRd6Le3tR70NG_g8d7jx9g1whgR-O1qvc8v1M83X8HnsRgjwBTwjA0molKlUvB-_kdfslHOnwCAFZdS1QN2t6KUd-S60FPRxmJr3DpEKjZkUgzxo2jaVBjfm-h-vmNPsSW3NjG4fMUuGrPJNPq9Q_Y2f3idLcrl8-PT7H5ZOiFUV3IEklyoKQkyKK00tfUG0DbgwTleUc2tkk4J2VjwE-MbZaEGDii9dYIP2c0pt81d0NmF7rDAtTEehmusAaHGg6k6mVxqc07U6F0KW5P2GkEfYel_sLTQJ1j8G07nYa0</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Perspective on machine learning for advancing fluid mechanics</title><source>American Physical Society Journals</source><creator>Brenner, M. P. ; Eldredge, J. D. ; Freund, J. B.</creator><creatorcontrib>Brenner, M. P. ; Eldredge, J. D. ; Freund, J. B. ; Univ. of Illinois at Urbana-Champaign, IL (United States)</creatorcontrib><description>In this paper, a perspective is presented on how machine learning (ML), with its burgeoning popularity and the increasing availability of portable implementations, might advance fluid mechanics. As with any numerical or experimental method, ML methods have strengths and limitations, which are acknowledged. Their potential impact is high so long as outcomes are held to the long-standing critical standards that should guide studies of flow physics.</description><identifier>ISSN: 2469-990X</identifier><identifier>EISSN: 2469-990X</identifier><identifier>DOI: 10.1103/PhysRevFluids.4.100501</identifier><language>eng</language><publisher>United States: American Physical Society (APS)</publisher><subject>ENGINEERING ; Physics</subject><ispartof>Physical review fluids, 2019-10, Vol.4 (10), Article 100501</ispartof><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c449t-310e73495e4ea17b7a8bda01bf0d0cc36e83b97c947fb0d2adf9b0803017dbc43</citedby><cites>FETCH-LOGICAL-c449t-310e73495e4ea17b7a8bda01bf0d0cc36e83b97c947fb0d2adf9b0803017dbc43</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,315,781,785,886,2877,2878,27929,27930</link.rule.ids><backlink>$$Uhttps://www.osti.gov/servlets/purl/1801081$$D View this record in Osti.gov$$Hfree_for_read</backlink></links><search><creatorcontrib>Brenner, M. P.</creatorcontrib><creatorcontrib>Eldredge, J. D.</creatorcontrib><creatorcontrib>Freund, J. B.</creatorcontrib><creatorcontrib>Univ. of Illinois at Urbana-Champaign, IL (United States)</creatorcontrib><title>Perspective on machine learning for advancing fluid mechanics</title><title>Physical review fluids</title><description>In this paper, a perspective is presented on how machine learning (ML), with its burgeoning popularity and the increasing availability of portable implementations, might advance fluid mechanics. As with any numerical or experimental method, ML methods have strengths and limitations, which are acknowledged. Their potential impact is high so long as outcomes are held to the long-standing critical standards that should guide studies of flow physics.</description><subject>ENGINEERING</subject><subject>Physics</subject><issn>2469-990X</issn><issn>2469-990X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNpVkE9LAzEQxYMoWGq_gizet8406WZz8CDFWqFgEQVvIX9mbaTNlmRd6Le3tR70NG_g8d7jx9g1whgR-O1qvc8v1M83X8HnsRgjwBTwjA0molKlUvB-_kdfslHOnwCAFZdS1QN2t6KUd-S60FPRxmJr3DpEKjZkUgzxo2jaVBjfm-h-vmNPsSW3NjG4fMUuGrPJNPq9Q_Y2f3idLcrl8-PT7H5ZOiFUV3IEklyoKQkyKK00tfUG0DbgwTleUc2tkk4J2VjwE-MbZaEGDii9dYIP2c0pt81d0NmF7rDAtTEehmusAaHGg6k6mVxqc07U6F0KW5P2GkEfYel_sLTQJ1j8G07nYa0</recordid><startdate>20191016</startdate><enddate>20191016</enddate><creator>Brenner, M. P.</creator><creator>Eldredge, J. D.</creator><creator>Freund, J. B.</creator><general>American Physical Society (APS)</general><scope>AAYXX</scope><scope>CITATION</scope><scope>OIOZB</scope><scope>OTOTI</scope></search><sort><creationdate>20191016</creationdate><title>Perspective on machine learning for advancing fluid mechanics</title><author>Brenner, M. P. ; Eldredge, J. D. ; Freund, J. B.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c449t-310e73495e4ea17b7a8bda01bf0d0cc36e83b97c947fb0d2adf9b0803017dbc43</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>ENGINEERING</topic><topic>Physics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Brenner, M. P.</creatorcontrib><creatorcontrib>Eldredge, J. D.</creatorcontrib><creatorcontrib>Freund, J. B.</creatorcontrib><creatorcontrib>Univ. of Illinois at Urbana-Champaign, IL (United States)</creatorcontrib><collection>CrossRef</collection><collection>OSTI.GOV - Hybrid</collection><collection>OSTI.GOV</collection><jtitle>Physical review fluids</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Brenner, M. P.</au><au>Eldredge, J. D.</au><au>Freund, J. B.</au><aucorp>Univ. of Illinois at Urbana-Champaign, IL (United States)</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Perspective on machine learning for advancing fluid mechanics</atitle><jtitle>Physical review fluids</jtitle><date>2019-10-16</date><risdate>2019</risdate><volume>4</volume><issue>10</issue><artnum>100501</artnum><issn>2469-990X</issn><eissn>2469-990X</eissn><abstract>In this paper, a perspective is presented on how machine learning (ML), with its burgeoning popularity and the increasing availability of portable implementations, might advance fluid mechanics. As with any numerical or experimental method, ML methods have strengths and limitations, which are acknowledged. Their potential impact is high so long as outcomes are held to the long-standing critical standards that should guide studies of flow physics.</abstract><cop>United States</cop><pub>American Physical Society (APS)</pub><doi>10.1103/PhysRevFluids.4.100501</doi><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 2469-990X
ispartof Physical review fluids, 2019-10, Vol.4 (10), Article 100501
issn 2469-990X
2469-990X
language eng
recordid cdi_osti_scitechconnect_1801081
source American Physical Society Journals
subjects ENGINEERING
Physics
title Perspective on machine learning for advancing fluid mechanics
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-14T09%3A58%3A18IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-crossref_osti_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Perspective%20on%20machine%20learning%20for%20advancing%20fluid%20mechanics&rft.jtitle=Physical%20review%20fluids&rft.au=Brenner,%20M.%20P.&rft.aucorp=Univ.%20of%20Illinois%20at%20Urbana-Champaign,%20IL%20(United%20States)&rft.date=2019-10-16&rft.volume=4&rft.issue=10&rft.artnum=100501&rft.issn=2469-990X&rft.eissn=2469-990X&rft_id=info:doi/10.1103/PhysRevFluids.4.100501&rft_dat=%3Ccrossref_osti_%3E10_1103_PhysRevFluids_4_100501%3C/crossref_osti_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true