{"id":19372,"date":"2025-04-11T13:09:15","date_gmt":"2025-04-11T13:09:15","guid":{"rendered":"https:\/\/aerospacerepository.org\/?p=19372"},"modified":"2025-09-09T13:48:47","modified_gmt":"2025-09-09T13:48:47","slug":"machine-learning-based-parametric-model-order-reduction-for-the-gust-load-analysis","status":"publish","type":"post","link":"https:\/\/aerospacerepository.org\/index.php\/2025\/04\/11\/machine-learning-based-parametric-model-order-reduction-for-the-gust-load-analysis\/","title":{"rendered":"Machine learning-based parametric model order reduction for the gust load analysis"},"content":{"rendered":"\n<p><strong>Sangmin Lee, SiHun Lee, Younggeun Park, Seung-Hoon Kang, Kijoo Jang, Haeseong Cho, SangJoon Shin<\/strong><\/p>\n\n\n\n<p><strong>DOI Number: https:\/\/doi.org\/10.82439\/ceas-ifasd-2024-135<\/strong><\/p>\n\n\n\n<p><strong>Conference number: IFASD-2024-135<\/strong><\/p>\n\n\n\n<p>This paper presents an efficient nonlinear non-intrusive model order reduction (MOR) framework for the gust load analysis. The proposed method is based on artificial neural network (ANN), specifically a least-square hierarchical variational autoencoder (LSH-VAE). This approach will enable construction of nonlinear reduced-order model and allow accurate interpolation with regard to the parameters. The proposed method will be validated by applying for a high-altitude long-endurance (HALE) unmanned aerial vehicle (UAV). The accuracy and computational efficiency of the method will be compared against those by a full order model (FOM). It is found that the proposed method will construct accurate interpolated field with regard to the relevant parameters.<\/p>\n\n\n\n<p><a href=\"https:\/\/aerospacerepository.org\/wp-content\/uploads\/2025\/04\/135.pdf\">Read the full paper here<\/a><\/p>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p><b>Sangmin Lee, SiHun Lee, Younggeun Park, Seung-Hoon Kang, Kijoo Jang, Haeseong Cho, SangJoon Shin<\/b><\/p>\n<p>DOI Number: https:\/\/doi.org\/10.82439\/ceas-ifasd-2024-135<\/p>\n<p>Conference number: IFASD-2024-135<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[993,3022,3041],"tags":[3156,1224,3155],"class_list":["post-19372","post","type-post","status-publish","format-standard","hentry","category-events","category-ifasd-2024","category-reduced-order-models-1-ifasd-2024","tag-gust-load-analysis","tag-machine-learning","tag-nonlinear-parametric-model-order-reduction","category-993","category-3022","category-3041","description-off"],"_links":{"self":[{"href":"https:\/\/aerospacerepository.org\/index.php\/wp-json\/wp\/v2\/posts\/19372","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/aerospacerepository.org\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/aerospacerepository.org\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/aerospacerepository.org\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/aerospacerepository.org\/index.php\/wp-json\/wp\/v2\/comments?post=19372"}],"version-history":[{"count":2,"href":"https:\/\/aerospacerepository.org\/index.php\/wp-json\/wp\/v2\/posts\/19372\/revisions"}],"predecessor-version":[{"id":20304,"href":"https:\/\/aerospacerepository.org\/index.php\/wp-json\/wp\/v2\/posts\/19372\/revisions\/20304"}],"wp:attachment":[{"href":"https:\/\/aerospacerepository.org\/index.php\/wp-json\/wp\/v2\/media?parent=19372"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/aerospacerepository.org\/index.php\/wp-json\/wp\/v2\/categories?post=19372"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/aerospacerepository.org\/index.php\/wp-json\/wp\/v2\/tags?post=19372"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}