[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118750-en":3,"doc-seo-118750-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118750,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","The transformative potential of machine learning for experiments in fluid mechanics","Machine learning is reshaping experimental fluid dynamics by improving how real flow phenomena are measured, modeled, and acted upon. The perspective outlines three main avenues: augmenting measurement fidelity through learned corrections and data enhancement (including super-resolution and noise removal), advancing experimental design and surrogate digital twins via modeling and active learning, and enabling real-time estimation and control through AI/ML. It reviews recent successes, open challenges, and key caveats and limitations while pointing to new ML-augmented experimental capabilities.","arXiv :2303 . 15832v2 [physics .flu-dyn] 29 Mar 2023  \nThe transformative potential of machine learning for experiments in fluid mechanics  \nRicardo Vinuesa 1 ;2􀀃, Steven L. Brunton3 and Beverley J. McKeon4  \n1FLOW, Engineering Mechanics, KTH Royal Institute of Technology, Stockholm, Sweden  \n2Swedish e-Science Research Centre (SeRC), Stockholm, Sweden  \n3Department of Mechanical Engineering, University of Washington, Seattle, WA, USA  \n4Department of Mechanical Engineering, Stanford University, Stanford, CA 94305  \nAbstract  \nThe field of machine learning has rapidly advanced the state of the art in many fields of science and engineering, including experimental fluid dynamics, which is one of the original big-data disciplines. This perspective will highlight several aspects of experimental fluid mechanics that stand to benefit from progress advances in machine learning, including: 1) augmenting the fidelity and quality of measurement techniques, 2) improving experimental design and surrogate digital-twin models and 3) enabling real-time estimation and control. In each case, we discuss recent success stories and ongoing challenges, along with caveats and limitations, and outline the potential for new avenues of ML-augmented and ML-enabled experimental fluid mechanics.  \nIntroduction  \nThe current renaissance in machine learning has the potential to revolutionize many aspects of the human experience, including scientific discovery. The ability to learn from data is developing synergetically with the generation of big data in a range of applications, as well as rapid advances in materials science and additive manufacturing. In this article, we describe some frontiers impacted or opened by machine learning (ML) applied to experimentation in fluid mechanics, a discipline at the core of many applications and recent technological developments in health, transportation, energy, and the environment.  \nFrom da Vinci's sketches of eddies forming in a pool to present-day qualitative and quantitative observations of flow through or over complex geometries, experiments are often either the first means of discovery, or the only means to reach extreme flow conditions in advance of numerical development. They may serve to complement, validate or extend theory and simulation. However, the benefits of observing the “truth”, i.e. the real-world manifestation of flow phenomena, are usually accompanied by challenges associated with non-ideal experimental conditions (noise, vibration, thermal drift, etc.) and/or diagnostic limitations (limited spatial and/or temporal resolution, sensor accuracy, bias, etc.) . For example, the study of turbulence near walls, of fundamental importance to a range of engineering applications as well as a foundational physics problem, is hampered by the physical dimensions of even the smallest (intrusive or non-intrusive) probe as the Reynolds number increases and the smallest turbulent scales decrease in size. This particular problem has given rise to a range of empirical correlations, which may then be used in efforts to differentiate nuanced scaling arguments. In industrial settings, experiments form critical design and testing steps, even in the age of advanced computational capabilities. ML has been increasingly adopted as a core technology in the field [14, 18, 104], with a focus on computational fluid dynamics (CFD) [109] . In this study we consider the potential impact of ML on experimental capabilities with a particular emphasis on the multiscale nature of turbulent flow, which may pose specific challenges to classical approaches.  \nWe divide the experimental-fluid-mechanics applications into three different categories, ordered in terms of increasing novelty of the ML-enabled capability to the field: 1) augmentation of the fidelity of measurement techniques, for example by replacing empirical corrections with generalizable schemes learned from data and increasing what can be learned from observation through super-resolution or noise","cbCaihIwdvCHjdK5","https://ap.wps.com/l/cbCaihIwdvCHjdK5","pdf",1818417,1,13,"English","en",105,"# Introduction\n## Experimental fluid mechanics challenges and opportunities\n## ML-enabled application categories\n# Complexity of ML implementation\n## Active learning and digital twins\n## Non-intrusive sensing and rheology estimation\n## Super resolution and data enhancement","[{\"question\":\"ML如何提升实验流体力学的测量精度与质量？\",\"answer\":\"通过用可泛化的数据学习方案替代经验修正，并借助超分辨率与噪声去除等数据增强方法，从而提高从观测中可获得的信息量。\"},{\"question\":\"文中将ML赋能的实验流体力学应用分为哪三类？\",\"answer\":\"三类分别是：测量保真度提升（测量技术增强）、建模/主动学习/实验设计（含数字孪生），以及AI/ML可能带来的估计与控制能力。\"},{\"question\":\"为何在湍流等问题上实验观测会面临困难？\",\"answer\":\"非理想实验条件与诊断限制会影响观测质量，例如噪声、振动、热漂移，以及空间/时间分辨率和传感器精度等约束；湍流近壁测量还会因探针尺寸与雷诺数升高而受限。\"}]","The transformative potential of machine learning for experiments in fluid mechanics | PDF",1785720044,33,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"the-transformative-potential-of-machine-learning-for-experiments-in-fluid-mechanics","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/the-transformative-potential-of-machine-learning-for-experiments-in-fluid-mechanics/118750/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"ML如何提升实验流体力学的测量精度与质量？","Question",{"text":76,"@type":77},"通过用可泛化的数据学习方案替代经验修正，并借助超分辨率与噪声去除等数据增强方法，从而提高从观测中可获得的信息量。","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"文中将ML赋能的实验流体力学应用分为哪三类？",{"text":81,"@type":77},"三类分别是：测量保真度提升（测量技术增强）、建模/主动学习/实验设计（含数字孪生），以及AI/ML可能带来的估计与控制能力。",{"name":83,"@type":74,"acceptedAnswer":84},"为何在湍流等问题上实验观测会面临困难？",{"text":85,"@type":77},"非理想实验条件与诊断限制会影响观测质量，例如噪声、振动、热漂移，以及空间/时间分辨率和传感器精度等约束；湍流近壁测量还会因探针尺寸与雷诺数升高而受限。","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story 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