[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120655-en":3,"doc-seo-120655-105":30,"detail-sidebar-cat-0-en-105":91},{"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},120655,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Emerging trends in machine learning for computational fluid dynamics - research overview","Machine learning is attracting renewed attention as a source of new research opportunities for computational fluid dynamics. The work highlights how emerging ML trends can improve CFD, focusing on demonstrated synergies and areas still under development with potential benefits in the coming years. A balanced perspective is emphasized, combining optimism about capabilities with caution regarding risks and limitations when transferring ML approaches into fluid-mechanics workflows.","arXiv :2211 . 15145v1 [physics .flu-dyn] 28 Nov 2022  \nEmerging trends in machine learning for computational 􀀃uid dynamics  \nRicardo Vinuesa 1 ,2􀀃 and Steven L. Brunton3  \n1 FLOW, Engineering Mechanics, KTH Royal Institute of Technology, Stockholm, Sweden  \n2 Swedish e-Science Research Centre (SeRC), Stockholm, Sweden  \n3 Department of Mechanical Engineering, University of Washington, Seattle, WA 98195, United States  \nAbstract  \nThe renewed interest from the scienti􀀂c community in machine learning (ML) is opening many new areas of research. Here we focus on how novel trends in ML are providing opportunities to improve the 􀀂eld of computational  \n􀀃uid dynamics (CFD) . In particular, we discuss synergies between ML and CFD that have already shown bene􀀂ts, and we also assess areas that are under development and may produce important bene􀀂ts in the coming years.  \nWe believe that it is also important to emphasize a balanced perspective of cautious optimism for these emerging approaches.  \nKeywords: machine learning (ML); deep learning (DL); arti􀀂cial intelligence (AI); computational 􀀃uid dynamics (CFD); direct numerical simulation (DNS); large-eddy simulation (LES); Reynolds-averaged Navier–Stokes (RANS);  \nreduced-order model (ROM)  \n1 Introduction  \nMachine learning (ML) is a rapidly developing 􀀂eld of research that has already transformed the state-of-the-art performance capabilities for many traditional tasks in computer science, such as image classi􀀂cation and captioning, natural language processing, and recommender systems. The numerous success stories of machine learning (ML) have led to widespread adoption in the scienti􀀂c and engineering communities as well, fueled by a growing wealth of data, computing resources, and advanced optimization algorithms; this is especially true in the 􀀂eld of 􀀃uid mechanics [2] . These emerging technologies complement existing computational and experimental methods, providing a uni􀀂ed approach to building models from data.  \nIncreasingly, ML is also being used to enhance and augment existing scienti􀀂c-computing paradigms. In particular, the 􀀂eld of computational 􀀃uid dynamics (CFD) is currently bene􀀂ting from accelerated high-􀀂delity simulations, improved turbulence modeling, and enhanced development of reduced-order models (ROMs) thanks to the latest developments in ML. Some of these applications include the following:  \n• Modeling the near-wall region of wall-bounded turbulence. Milano and Koumoutsakos [9] developed, 20 years ago, a method to predict the relevant features of turbulent channel 􀀃ow close to the wall by using deep neural networks. In addition to the interest of this work from the perspective of modeling turbulence, and potentially developing wall models, the authors also established connections between neural networksand traditional methods such as proper-orthogonal decomposition (POD). In particular, they showed that by restricting the neural network model tobe linear, the network essentially learns the features produced by standard POD. This topic has received renewed attention in recent years due to the possibility of training deeper neural network models with larger data sets, as well as the emergence of novel learning architectures [1] .  \n• Development of in􀀃ow conditions for turbulence simulations. This is a critical area for CFD when it comes to achieving high-Reynolds-number (Re) conditions and simulating complex geometries. Spatiallydeveloping turbulent boundary layers (TBLs) require very long domains to reach high Reynolds numbers, and if part of the low-Re region can be replaced by an adequate in􀀃ow condition, simulations at the relevant high-Re regime can become feasible. Similarly, if a simulation is designed to study the turbulent 􀀃ow around a complex array of obstacles, it is bene􀀂cial to replace the in􀀃ow section of the simulation by a suitable in-􀀃ow condition, thus yielding signi􀀂cant computational savings. Traditional approaches to these in􀀃ows have relie","cbCaigEHITEgFUtB","https://ap.wps.com/l/cbCaigEHITEgFUtB","pdf",127648,1,7,"English","en",105,"# Introduction\n## ML for CFD: opportunities and synergies\n## Near-wall turbulence modeling\n## In-flow conditions for turbulence simulations\n## Boundary conditions for external flows\n## Subgrid-scale models for LES","[{\"question\":\"What is the main focus of the document?\",\"answer\":\"It focuses on how emerging machine learning trends create opportunities to improve computational fluid dynamics, including both already demonstrated benefits and areas under development.\"},{\"question\":\"Which CFD problems does the document connect with ML applications?\",\"answer\":\"It discusses near-wall turbulence modeling, development of inflow conditions, design of boundary conditions for external flows, and improved subgrid-scale models for large-eddy simulations.\"},{\"question\":\"What perspective does the document recommend when adopting these emerging ML approaches?\",\"answer\":\"It argues for a balanced stance of cautious optimism, recognizing both potential benefits and the need to carefully assess assumptions, accuracy, and limitations.\"}]","Emerging trends in machine learning for computational fluid dynamics - 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