{"id":19633,"date":"2025-05-12T13:22:15","date_gmt":"2025-05-12T13:22:15","guid":{"rendered":"https:\/\/aerospacerepository.org\/?p=19633"},"modified":"2025-09-10T10:27:36","modified_gmt":"2025-09-10T10:27:36","slug":"machine-learning-to-forecast-unsteady-motion-induced-unsteady-forces","status":"publish","type":"post","link":"https:\/\/aerospacerepository.org\/index.php\/2025\/05\/12\/machine-learning-to-forecast-unsteady-motion-induced-unsteady-forces\/","title":{"rendered":"Machine learning to forecast unsteady, motion-induced unsteady forces"},"content":{"rendered":"\n<p><strong>Reik Thormann, Hans Martin Bleecke<\/strong><\/p>\n\n\n\n<p><strong>DOI Number: https:\/\/doi.org\/10.82439\/ceas-ifasd-2024-030<\/strong><\/p>\n\n\n\n<p><strong>Conference number: IFASD-2024-030<\/strong><\/p>\n\n\n\n<p>In this paper, the combination of an auto-encoder coupled with a transformer is presented to predict unsteady surface pressures due to forced excitation. While the used neural-network architecture operates in the the time domain, training data are computed with the linearized, frequency-domain CFD solver for a generic, high-aspect-ratio wing. These data are transfered into the time-domain to train the network, while the network\u2019s outputs are Fourier transformed and compared to their corresponding reference. Results are compared for unsteady, local surface pressures, frequency response functions of the generalized aerodynamic force matrices as well as flutter predictions.<\/p>\n\n\n\n<p><a href=\"https:\/\/aerospacerepository.org\/wp-content\/uploads\/2025\/05\/30.pdf\">Read the full paper here<\/a><\/p>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p><b>Reik Thormann, Hans Martin Bleecke<\/b><\/p>\n<p>DOI Number: https:\/\/doi.org\/10.82439\/ceas-ifasd-2024-030<\/p>\n<p>Conference number: IFASD-2024-030<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[993,3022,3042],"tags":[975,2567,1224,2001],"class_list":["post-19633","post","type-post","status-publish","format-standard","hentry","category-events","category-ifasd-2024","category-unsteady-aerodynamics-1-ifasd-2024","tag-flutter","tag-high-aspect-ratio-wing","tag-machine-learning","tag-unsteady-aerodynamics","category-993","category-3022","category-3042","description-off"],"_links":{"self":[{"href":"https:\/\/aerospacerepository.org\/index.php\/wp-json\/wp\/v2\/posts\/19633","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=19633"}],"version-history":[{"count":3,"href":"https:\/\/aerospacerepository.org\/index.php\/wp-json\/wp\/v2\/posts\/19633\/revisions"}],"predecessor-version":[{"id":20409,"href":"https:\/\/aerospacerepository.org\/index.php\/wp-json\/wp\/v2\/posts\/19633\/revisions\/20409"}],"wp:attachment":[{"href":"https:\/\/aerospacerepository.org\/index.php\/wp-json\/wp\/v2\/media?parent=19633"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/aerospacerepository.org\/index.php\/wp-json\/wp\/v2\/categories?post=19633"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/aerospacerepository.org\/index.php\/wp-json\/wp\/v2\/tags?post=19633"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}