{"id":1737,"date":"2020-11-07T17:14:06","date_gmt":"2020-11-07T16:14:06","guid":{"rendered":"https:\/\/www.netscientificjournals.com\/smart-platform\/?post_type=call_for_papers&#038;p=1737"},"modified":"2021-01-14T15:54:07","modified_gmt":"2021-01-14T14:54:07","slug":"machine-learning-methods-for-complex-flows","status":"publish","type":"call_for_papers","link":"https:\/\/www.netscientificjournals.com\/smart-platform\/?call_for_papers=machine-learning-methods-for-complex-flows","title":{"rendered":"Machine-Learning Methods for Complex Flows"},"content":{"rendered":"<p>We would like to invite you to contribute to a Special Issue of\u00a0Energies\u00a0on the subject area of <strong>Machine-Learning Applications to Complex Flows<\/strong>. We are experiencing a rapid development of efficient data-driven methods to predict, analyze and simulate a wide range of complex turbulent flows. Our aim is to provide a complete view on the potential of these methods in the coming years, both for researchers and practitioners.\u00a0This Special Issue will deal with novel data-driven techniques to study complex flows. \u00a0Topics of interest for publication include, but are not limited to:\u00a0Neural networks; Bayesian regression;\u00a0Gaussian processes;\u00a0Uncertainty quantification;\u00a0Optimization;\u00a0Flow reconstruction;\u00a0Remote sensing;\u00a0Structure identification;\u00a0Dynamical systems;\u00a0Modal decompositions;\u00a0Sustainability.<br \/>\nKeywords: Machine learning;\u00a0Artificial intelligence;\u00a0Turbulent flows;\u00a0Numerical simulation;\u00a0Experimental techniques;\u00a0Modal decompositions.<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"template":"","format":"standard","topic":[192,627,644,576,597,625,453,545,504],"country":[55],"nsj_language":[57],"class_list":["post-1737","call_for_papers","type-call_for_papers","status-publish","format-standard","hentry","topic-artificial-intelligence","topic-computational-aerodynamics","topic-data-analysis-processes","topic-data-management","topic-data-sensing-and-analysis","topic-dynamic-models","topic-environmental-sustainability","topic-machine-learning","topic-sustainability","country-switzerland","nsj_language-english"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Machine-Learning Methods for Complex Flows - NetScientficJournals.com - Science Magazines, Events and More<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.netscientificjournals.com\/smart-platform\/?call_for_papers=machine-learning-methods-for-complex-flows\" class=\"yoast-seo-meta-tag\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" class=\"yoast-seo-meta-tag\" \/>\n<meta property=\"og:type\" content=\"article\" class=\"yoast-seo-meta-tag\" \/>\n<meta property=\"og:title\" content=\"Machine-Learning Methods for Complex Flows - NetScientficJournals.com - Science Magazines, Events and More\" class=\"yoast-seo-meta-tag\" \/>\n<meta property=\"og:description\" content=\"We would like to invite you to contribute to a Special Issue of\u00a0Energies\u00a0on the subject area of Machine-Learning Applications to Complex Flows. 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