[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117224-en":3,"doc-seo-117224-105":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":4,"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},117224,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Challenges and Opportunities for Machine Learning in Fluid Mechanics","Big data and machine learning accelerate economic and social change by reshaping how applied scientists build models and solve scientific tasks. Machine learning learns input-output functions from data with minimal prior knowledge, becoming increasingly viable as experimental and numerical techniques deliver higher-quality measurements. These notes describe integrating machine learning with classic fluid-dynamics methods, framing multiple fluid-mechanics tasks as machine-learning problems, and examining challenges alongside concrete opportunities and applications across prediction, turbulence modeling, reduced-order methods, meshless PDE learning, super-resolution, and flow control.","arXiv :2202 . 12577v3 [physics .flu-dyn] 13 Apr 2024  \nChallenges and Opportunities for Machine Learning in  \nFluid Mechanics  \nM. A. Mendez, J. Dominique, M. Fiore, F. Pino,  \nP. Sperotto, J. Van den Berghe  \nvon Karman Institute for Fluid Dynamics  \nAbstract  \nBig data and machine learning are driving comprehensive economic and social transformations and are rapidly re-shaping the toolbox and the methodologies of applied scientists. Machine learning tools are designed to learn functions from data with little to no need of prior knowledge. As continuous developments in experimental and numerical methods improve our ability to collect high-quality data, machine learning tools become increasingly viable and promising also in disciplines rooted in physical principles. These notes explore how machine learning can be integrated and combined with more classic methods in fluid dynamics. After a brief review of the machine learning landscape, we frame various problems in fluid mechanics as machine learning problems and we explore challenges and opportunities. We consider several relevant applications: aeroacoustic noise prediction, turbulence modelling, reduced-order modelling and forecasting, meshless integration of (partial) differential equations, super-resolution and flow control. While this list is by no means exhaustive, the presentation will provide enough concrete examples to offer perspectives on how machine learning might impact the way we do research and learn from data.  \nKeywords  \nMachine Learning for Fluid Dynamics, Turbulence Modeling, Aeroacoustics Noise Prediction, Dimensionality Reduction, Reinforcement Learning, Meshless Methods for PDEs.  \n1 What is Machine Learning?  \nMachine learning is a subset of Artificial Intelligence (AI) at the intersection of computer science, statistics, engineering, neuroscience, and biology. The term ‘machine learning’was coined by Samuel (1959), to refer to the field of study that gives the computers the ability to learn without being explicitly programmed. A more precise, engineering-oriented definition by Mitchell (1997) reads: A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E. Let us delve into this definition.  \nFocusing on engineering applications, we replace the subject ‘computer program’ with‘model’. The learning of a model begins with the definition of a task (T): recognizing faces  \nVKI -1-  \nin images or predict flow separation from operating conditions in an airfoil, learn how to play chess or learn how to stabilize an unstable flow. These tasks can be formulated asa function from an input x ∈ X to an output y ∈ Y. The function might define what output matches with the input (e.g. in image recognition) or what action to take when a system is at a given state (e.g. what the best next chess move is) .  \nThe second ingredient is experience (E), i.e. a collection of data points in X and Y. These data might be available from the beginning (as in supervised learning) or might be collected while learning (as in reinforcement learning) . The t˜hird ingredient is an hypothesis set, e.g. a parametric representation of th˜ e function f ≈ f (x, w) which depends  \non some parameters (weights) w ∈ W. The model f(x, w) = w0 +w1 x+w2 x2 is an example with three parameters and rather small capacity (the set of all parabolas); an Artificial Neural Network (ANN) is an example with thousands (or millions!) parameters and amuch larger capacity (potentially any function) . The set of weights, and the associated hypothesis set, define how the computer performs the task, i.e. what x − y association it makes, or what actions y it takes when in state x.  \nThe fourth ingredient is a learning algorithm, based on some performance measure P. This includes the definition of a cost function that must be minimized or a reward function that must be maximized. Learning is, in","cbCailYkmFrTz3a2","https://ap.wps.com/l/cbCailYkmFrTz3a2","pdf",4839274,1,22,"English","en",105,"# Abstract\n# What is Machine Learning?\n## Supervised Learning","[{\"question\":\"How does this document define machine learning in an engineering context?\",\"answer\":\"It presents machine learning as learning a model for a task, improving performance using experience data, a hypothesis set (e.g., parameters/weights), and a learning algorithm driven by a performance measure.\"},{\"question\":\"What are the key components of a machine-learning formulation described here?\",\"answer\":\"The text outlines a task T, experience E (data points), a hypothesis set f(x,w) with parameters w, and an optimization-based learning algorithm using a cost/reward objective.\"},{\"question\":\"Which fluid-mechanics applications does the document highlight for machine learning?\",\"answer\":\"It highlights aeroacoustic noise prediction, turbulence modeling, reduced-order modeling and forecasting, meshless integration of (partial) differential equations, super-resolution, and flow control.\"}]","Challenges and Opportunities for Machine Learning in Fluid Mechanics | 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does this document define machine learning in an engineering context?","Question",{"text":75,"@type":76},"It presents machine learning as learning a model for a task, improving performance using experience data, a hypothesis set (e.g., parameters/weights), and a learning algorithm driven by a performance measure.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What are the key components of a machine-learning formulation described here?",{"text":80,"@type":76},"The text outlines a task T, experience E (data points), a hypothesis set f(x,w) with parameters w, and an optimization-based learning algorithm using a cost/reward objective.",{"name":82,"@type":73,"acceptedAnswer":83},"Which fluid-mechanics applications does the document highlight for machine learning?",{"text":84,"@type":76},"It highlights aeroacoustic noise prediction, turbulence modeling, reduced-order modeling and forecasting, meshless integration of (partial) differential equations, super-resolution, and flow 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