[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127557-en":3,"doc-seo-127557-105":31,"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127557,687207020761,"Patrick","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",6,"Technology","An Old-Fashioned Framework for Machine Learning in Turbulence Modeling","The objective is to provide clear, well-motivated guidance to machine learning (ML) teams for turbulence modeling, while also supporting modeling beyond ML. Turbulence culture takes years to learn and is hard to convey; many ML papers create unusable proposals due to math/physics errors or severe overfitting. Turbulence models are often arbitrary, and key properties rely on non-trivial differential-equation analysis, limiting purely data-driven methods.","AN OLD-FASHIONED FRAMEWORK FOR MACHINE LEARNING  \nIN TURBULENCE MODELING  \nPhilippe Spalart  \nWoodinville, USA  \n[prspalart@gmail.com](prspalart@gmail.com)  \nAbstract  \nThe objective is to provide clear and well-motivated guidance to Machine Learning (ML) teams, founded on our experience in empirical turbulence modeling. Guidance is also needed for modeling outside ML. ML is not yet successful in turbulence modeling, and many papers have produced unusable proposals either due to errors in math or physics, or to severe overfitting. We believe that “Turbulence Culture”(TC) takes years to learn and is difficult to convey especially considering the modern lack of time for careful study; important facts which are self-evident after a career in turbulence research and modeling and extensive reading are easy to miss. In addition, many of them are not absolute facts, a consequence of the gaps in our understanding of turbulence and the weak connection of models to first principles. Some of the mathematical facts are rigorous, but the physical aspects often are not. Turbulence models are surprisingly arbitrary. Disagreement between experts confuses the new entrants. In addition, several key properties of the models are ascertained through non-trivial analytical properties of the differential equations, which puts them out of reach of purely data-driven ML-type approaches. The best example is the crucial behavior of the model at the edge of the turbulent region (ETR) . The knowledge we wish to put out here may be divided into “Mission” and“Requirements,” each combining physics and mathematics. Clear lists of“Hard” and“Soft” constraints are presented. A concrete example of how DNS data could be used, possibly allied with ML, is first carried through and illustrates the large number of decisions needed. Our focus is on creating effective products which will empower CFD, rather than on publications.  \nIntroduction  \nTurbulence modeling has a mixed reputation. Many observers regret how far it is from perfection and describe a sense of stagnation, which by one simple measure goes back to 1992 [1, 2] . The community’s expectations have risen considerably since then. Very few people are active in the creation or improvement of models, compared with those engaged in CFD code improvement, some of them for profit; the investment into computers is also massive. Some major early contributors to modeling have withdrawn to neighboring fields.  \nThis is concurrent to the relentless progress in numerical accuracy. For decades, it was propelled by Moore’s Law; nowadays it rests just as much on progress in strong high-order solvers and grid adaptation. Whereas for many years, turbulencemodeling flaws could not be conclusively distinguished in practical work or even in semi-complex workshop cases from lack of numerical convergence, we are nearing the era of negligible numerical errors for steady-state flow solutions, and this even for complex geometries such as an airplane wing with multiple slats, flaps, and their supports [3] . Secondary sources of error such as elastic wing and flap support deformation and wind-tunnel effects are also better controlled. This will leave as the only culprit the errors associated with turbulence treatments. They will be finite, and larger than what would be acceptable in industry.  \nAnticipating this situation suggests increased strategic funding for modeling and a look for alternate approaches to that in force since the 1970s. Machine Learning being so successful in other fields, it is an obvious candidate, and the last 5 to 10 years duly have seen a massive rise in activity and publication [4-6] . However, some paper titles exhibit hubris and a grave ignorance of how hardened the turbulence problem has become [7] . This activity certainly has not had immediate success. To the author’s knowledge, none of it has produced a new model or model version that has been inserted into the Turbulence Modeling Resource [8], NASA and","cbCaippoLjTuq1iW","https://ap.wps.com/l/cbCaippoLjTuq1iW","pdf",550546,2,1,27,"English","en",105,"# Abstract\n# Introduction\n## Turbulence modeling reputation and stagnation\n## Numerical accuracy progress and remaining turbulence-treatment errors\n## Rise of ML activity and lack of production-ready model uptake\n## Funding, data production, and missing cross-fertilization\n# Turbulence-Resolving Simulations","[{\"question\":\"Why do many ML papers fail in turbulence modeling?\",\"answer\":\"Many proposals become unusable due to errors in math or physics, or severe overfitting, and they often do not produce a generalizable, codable product.\"},{\"question\":\"What makes turbulence modeling difficult beyond purely data-driven ML?\",\"answer\":\"Several key model properties are established through non-trivial analytical behavior of differential equations, which is not easily captured by purely data-driven approaches.\"},{\"question\":\"How does the framework propose to guide ML work?\",\"answer\":\"It divides knowledge into “Mission” and “Requirements,” presenting clear lists of “Hard” and “Soft” constraints that combine physics and mathematics, and walks through a concrete DNS-data usage example.\"}]","An Old-Fashioned Framework for Machine Learning in Turbulence Modeling | 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do many ML papers fail in turbulence modeling?","Question",{"text":76,"@type":77},"Many proposals become unusable due to errors in math or physics, or severe overfitting, and they often do not produce a generalizable, codable product.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What makes turbulence modeling difficult beyond purely data-driven ML?",{"text":81,"@type":77},"Several key model properties are established through non-trivial analytical behavior of differential equations, which is not easily captured by purely data-driven approaches.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the framework propose to guide ML work?",{"text":85,"@type":77},"It divides knowledge into “Mission” and “Requirements,” presenting clear lists of “Hard” and “Soft” constraints that combine physics and mathematics, and walks through a concrete DNS-data usage 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