{"id":21907,"date":"2025-04-03T20:31:19","date_gmt":"2025-04-03T20:31:19","guid":{"rendered":"https:\/\/www.tun.com\/home\/?p=21907"},"modified":"2025-04-03T20:31:20","modified_gmt":"2025-04-03T20:31:20","slug":"new-ai-model-to-speed-up-ocean-simulations","status":"publish","type":"post","link":"https:\/\/www.tun.com\/home\/new-ai-model-to-speed-up-ocean-simulations\/","title":{"rendered":"New AI Model to Speed Up Ocean Simulations"},"content":{"rendered":"\n<div class=\"wp-block-group\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<div class=\"wp-block-uagb-blockquote uagb-block-e7eb3fc3 uagb-blockquote__skin-border uagb-blockquote__stack-img-none\"><blockquote class=\"uagb-blockquote\"><div class=\"uagb-blockquote__content\">Researchers at Osaka Metropolitan University have created an AI-powered fluid simulation model that drastically cuts computation time while maintaining high accuracy, opening doors for advancements in offshore energy and maritime design.<\/div><footer><div class=\"uagb-blockquote__author-wrap uagb-blockquote__author-at-left\"><\/div><\/footer><\/blockquote><\/div>\n\n\n\n<div class=\"wp-block-group is-content-justification-space-between is-nowrap is-layout-flex wp-container-core-group-is-layout-0dfbf163 wp-block-group-is-layout-flex\"><div style=\"font-size:16px;\" class=\"has-text-align-left wp-block-post-author\"><div class=\"wp-block-post-author__content\"><p class=\"wp-block-post-author__name\">The University Network<\/p><\/div><\/div>\n\n\n<div class=\"wp-block-uagb-social-share uagb-social-share__outer-wrap uagb-social-share__layout-horizontal uagb-block-ee584a31\">\n<div class=\"wp-block-uagb-social-share-child uagb-ss-repeater uagb-ss__wrapper uagb-block-ec619ce7\"><span class=\"uagb-ss__link\" data-href=\"https:\/\/www.facebook.com\/sharer.php?u=\" tabindex=\"0\" role=\"button\" aria-label=\"facebook\"><span class=\"uagb-ss__source-wrap\"><span class=\"uagb-ss__source-icon\"><svg xmlns=\"https:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 512 512\"><path d=\"M504 256C504 119 393 8 256 8S8 119 8 256c0 123.8 90.69 226.4 209.3 245V327.7h-63V256h63v-54.64c0-62.15 37-96.48 93.67-96.48 27.14 0 55.52 4.84 55.52 4.84v61h-31.28c-30.8 0-40.41 19.12-40.41 38.73V256h68.78l-11 71.69h-57.78V501C413.3 482.4 504 379.8 504 256z\"><\/path><\/svg><\/span><\/span><\/span><\/div>\n\n\n\n<div class=\"wp-block-uagb-social-share-child uagb-ss-repeater uagb-ss__wrapper uagb-block-32d99934\"><span class=\"uagb-ss__link\" data-href=\"https:\/\/twitter.com\/share?url=\" tabindex=\"0\" role=\"button\" aria-label=\"twitter\"><span class=\"uagb-ss__source-wrap\"><span class=\"uagb-ss__source-icon\"><svg xmlns=\"https:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 512 512\"><path d=\"M389.2 48h70.6L305.6 224.2 487 464H345L233.7 318.6 106.5 464H35.8L200.7 275.5 26.8 48H172.4L272.9 180.9 389.2 48zM364.4 421.8h39.1L151.1 88h-42L364.4 421.8z\"><\/path><\/svg><\/span><\/span><\/span><\/div>\n\n\n\n<div class=\"wp-block-uagb-social-share-child uagb-ss-repeater uagb-ss__wrapper uagb-block-1d136f14\"><span class=\"uagb-ss__link\" data-href=\"https:\/\/www.linkedin.com\/shareArticle?url=\" tabindex=\"0\" role=\"button\" aria-label=\"linkedin\"><span class=\"uagb-ss__source-wrap\"><span class=\"uagb-ss__source-icon\"><svg xmlns=\"https:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 448 512\"><path d=\"M416 32H31.9C14.3 32 0 46.5 0 64.3v383.4C0 465.5 14.3 480 31.9 480H416c17.6 0 32-14.5 32-32.3V64.3c0-17.8-14.4-32.3-32-32.3zM135.4 416H69V202.2h66.5V416zm-33.2-243c-21.3 0-38.5-17.3-38.5-38.5S80.9 96 102.2 96c21.2 0 38.5 17.3 38.5 38.5 0 21.3-17.2 38.5-38.5 38.5zm282.1 243h-66.4V312c0-24.8-.5-56.7-34.5-56.7-34.6 0-39.9 27-39.9 54.9V416h-66.4V202.2h63.7v29.2h.9c8.9-16.8 30.6-34.5 62.9-34.5 67.2 0 79.7 44.3 79.7 101.9V416z\"><\/path><\/svg><\/span><\/span><\/span><\/div>\n<\/div>\n<\/div>\n<\/div><\/div>\n\n\n\n<p>A team of researchers at Osaka Metropolitan University has developed a new machine learning-powered fluid simulation model that drastically reduces computation time without sacrificing accuracy. This innovative approach promises significant advancements in offshore power generation, ship design and real-time ocean monitoring.<\/p>\n\n\n\n<p>Traditional methods of predicting fluid behavior, which are crucial for industries relying on wave and tidal energy, often require extensive computational resources. Particle methods, a common technique, simulate fluid flow behavior but demand considerable processing power and time. By streamlining and speeding up these simulations, AI-powered surrogate models are revolutionizing fluid dynamics research.<\/p>\n\n\n\n<p>But AI has its flaws.<\/p>\n\n\n\n<p>\u201cAI can deliver exceptional results for specific problems but often struggles when applied to different conditions,\u201d lead author Takefumi Higaki, an assistant professor at Osaka Metropolitan University\u2019s Graduate School of Engineering, said in a news release.<\/p>\n\n\n\n<p>To overcome these challenges, the research team developed a new surrogate model using deep learning technology called graph neural networks. They meticulously compared various training conditions to identify essential factors for high-precision fluid calculations. The team also evaluated the model&#8217;s adaptability to different simulation speeds, known as time step sizes, and various types of fluid movements.<\/p>\n\n\n\n<p>The results were consistent across a variety of fluid scenarios.<\/p>\n\n\n\n<p>\u201cOur model maintains the same level of accuracy as traditional particle-based simulations, throughout various fluid scenarios, while reducing computation time from approximately 45 minutes to just three minutes,\u201d Higaki added.<\/p>\n\n\n\n<p>This advancement marks a significant step forward in high-performance fluid simulation, providing a scalable and generalizable solution that balances accuracy and efficiency. The implications extend beyond theoretical research.<\/p>\n\n\n\n<p>\u201cFaster and more precise fluid simulations can mean a significant acceleration in the design process for ships and offshore energy systems,\u201d added Higaki. \u201cThey also enable real-time fluid behavior analysis, which could maximize the efficiency of ocean energy systems.\u201d<\/p>\n\n\n\n<p>The study, <a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0141118725000124\" target=\"_blank\" rel=\"noopener\" title=\"\">published<\/a> in Applied Ocean Research, highlights the transformative potential of this technology, with the ability to streamline maritime design processes and enhance real-time ocean monitoring capabilities, thereby driving innovations in sustainable ocean energy systems.<\/p>\n\n\n\n<div style=\"height:16px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p><strong>Source: <\/strong><a href=\"https:\/\/www.omu.ac.jp\/en\/info\/research-news\/entry-80286.html\" target=\"_blank\" rel=\"noopener\" title=\"\">Osaka Metropolitan University<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>A team of researchers at Osaka Metropolitan University has developed a new machine learning-powered fluid simulation model that drastically reduces computation time without sacrificing accuracy. This innovative approach promises significant advancements in offshore power generation, ship design and real-time ocean monitoring. Traditional methods of predicting fluid behavior, which are crucial for industries relying on wave [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"single-no-separators","format":"standard","meta":{"_acf_changed":false,"_uag_custom_page_level_css":"","_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[8],"tags":[262],"class_list":["post-21907","post","type-post","status-publish","format-standard","hentry","category-ai","tag-osaka-metropolitan-university"],"acf":[],"aioseo_notices":[],"uagb_featured_image_src":{"full":false,"thumbnail":false,"medium":false,"medium_large":false,"large":false,"1536x1536":false,"2048x2048":false},"uagb_author_info":{"display_name":"The University Network","author_link":"https:\/\/www.tun.com\/home\/author\/funky_junkie\/"},"uagb_comment_info":0,"uagb_excerpt":"A team of researchers at Osaka Metropolitan University has developed a new machine learning-powered fluid simulation model that drastically reduces computation time without sacrificing accuracy. This innovative approach promises significant advancements in offshore power generation, ship design and real-time ocean monitoring. Traditional methods of predicting fluid behavior, which are crucial for industries relying on wave&hellip;","_links":{"self":[{"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/posts\/21907","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/comments?post=21907"}],"version-history":[{"count":2,"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/posts\/21907\/revisions"}],"predecessor-version":[{"id":21913,"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/posts\/21907\/revisions\/21913"}],"wp:attachment":[{"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/media?parent=21907"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/categories?post=21907"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/tags?post=21907"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}