{"id":15097,"date":"2024-01-22T09:09:02","date_gmt":"2024-01-22T08:09:02","guid":{"rendered":"https:\/\/sano.science\/?post_type=research&#038;p=15097"},"modified":"2024-06-07T13:01:24","modified_gmt":"2024-06-07T11:01:24","slug":"verification-validation-uncertainty-quantification-of-simulation-models","status":"publish","type":"research","link":"https:\/\/sano.science\/research\/verification-validation-uncertainty-quantification-of-simulation-models\/","title":{"rendered":"Verification, Validation, Uncertainty\u200b Quantification of simulation models"},"content":{"rendered":"\n<p class=\" eplus-wrapper\">The project demonstrates how cloud computing and in particular novel serverless computing model can be applied to medical applications and complement traditional high-performance computing (HPC) approaches. Our work focused on development of CloudVVUQ library for running verification, validation and uncertainty quantification (VVUQ) pipelines for computational models on serverless infrastructures. VVUQ process plays an important role in the regulatory process of bringing computational models to the clinical market. Our research was published in a conference paper at CCGrid 2023, where we showed that for large studies with 50000 parameters (tasks) it is possible to reduce total time of computing from over 76 h when computing locally to nearly 12 min when using Google Cloud.<\/p>\n\n\n\n<p class=\" eplus-wrapper\"><a href=\"https:\/\/github.com\/SanoScience\/CloudVVUQ\" target=\"_blank\" rel=\"noreferrer noopener\">github.com\/SanoScience<\/a><\/p>\n\n\n\n<p class=\" eplus-wrapper\"><a href=\"https:\/\/ieeexplore.ieee.org\/document\/10171522\">ieeexplore.iee<\/a><a href=\"https:\/\/ieeexplore.ieee.org\/document\/10171522\" target=\"_blank\" rel=\"noreferrer noopener\">e.org<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>The project demonstrates how cloud computing and in particular novel serverless computing model can be applied to medical applications and complement traditional high-performance computing (HPC) approaches. Our work focused on development of CloudVVUQ library for running verification, validation and uncertainty quantification (VVUQ) pipelines for computational models on serverless infrastructures. VVUQ process plays an important role in the regulatory process of bringing computational models to the clinical market. Our research was published in a conference paper at CCGrid 2023, where we showed that for large studies with 50000 parameters (tasks) it is possible to reduce total time of computing from over 76 h when computing locally to nearly 12 min when using Google Cloud.<\/p>\n","protected":false},"featured_media":0,"template":"","research_type":[7],"research_team":[16],"class_list":["post-15097","research","type-research","status-publish","hentry","research_type-research-topics","research_team-extreme-scale-data-and-computing"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.4 (Yoast SEO v27.4) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>VVUQ<\/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:\/\/sano.science\/research\/verification-validation-uncertainty-quantification-of-simulation-models\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Verification, Validation, Uncertainty\u200b Quantification of simulation models\" \/>\n<meta property=\"og:description\" content=\"The project demonstrates how cloud computing and in particular novel serverless computing model can be applied to medical applications and complement traditional high-performance computing (HPC) approaches. Our work focused on development of CloudVVUQ library for running verification, validation and uncertainty quantification (VVUQ) pipelines for computational models on serverless infrastructures. VVUQ process plays an important role in the regulatory process of bringing computational models to the clinical market. Our research was published in a conference paper at CCGrid 2023, where we showed that for large studies with 50000 parameters (tasks) it is possible to reduce total time of computing from over 76 h when computing locally to nearly 12 min when using Google Cloud.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/sano.science\/research\/verification-validation-uncertainty-quantification-of-simulation-models\/\" \/>\n<meta property=\"og:site_name\" content=\"Centre for Computational Personalized Medicine\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/sano.science\/\" \/>\n<meta property=\"article:modified_time\" content=\"2024-06-07T11:01:24+00:00\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:site\" content=\"@sanoscience\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"1 minute\" \/>\n<script type=\"application\/ld+json\" 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