{"id":1112,"date":"2025-11-18T09:11:25","date_gmt":"2025-11-18T08:11:25","guid":{"rendered":"https:\/\/www.lgt.tf.fau.de\/?p=1112"},"modified":"2026-02-04T15:37:04","modified_gmt":"2026-02-04T14:37:04","slug":"new-preprint-on-fourier-based-neural-operators-for-simulating-mold-filling-processes","status":"publish","type":"post","link":"https:\/\/www.lgt.tf.fau.de\/en\/2025\/11\/18\/new-preprint-on-fourier-based-neural-operators-for-simulating-mold-filling-processes\/","title":{"rendered":"New\u00a0preprint\u00a0on\u00a0Fourier-based\u00a0neural\u00a0operators\u00a0for\u00a0simulating\u00a0mold\u00a0filling\u00a0processes"},"content":{"rendered":"\n<p>The Chair of Casting Technology presents a new preprint: \u201cFourier Neural Operators for Two-Phase, 2D Mold-Filling Problems Related to Metal Casting.\u201d In this article, we demonstrate how mold filling processes can be predicted extremely quickly and accurately using Fourier-based neural operators. The\u00a0data-driven model achieves errors of around 5 % and is two to three orders of magnitude faster than classic CFD simulations. This\u00a0makes it ideal for integration into the ongoing design and optimization of mold filling processes. The method is not limited to metal casting. It can be applied to a wide range of filling problems involving complex multiphase flows and enables rapid variant studies without complete CFD calculations. The\u00a0preprint\u00a0is\u00a0freely available at this link: <a href=\"https:\/\/doi.org\/10.48550\/arXiv.2510.25697\">https:\/\/doi.org\/10.48550\/arXiv.2510.25697<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The Chair of Casting Technology presents a new preprint: \u201cFourier Neural Operators for Two-Phase, 2D Mold-Filling Problems Related to Metal Casting.\u201d In this article, we demonstrate how mold filling processes can be predicted extremely quickly and accurately using Fourier-based neural operators. The\u00a0data-driven model achieves errors of around 5 % and is two to three orders [&hellip;]<\/p>\n","protected":false},"author":886,"featured_media":1402,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_rrze_cache":"enabled","_rrze_multilang_single_locale":"en_GB","_rrze_multilang_single_source":"https:\/\/www.lgt.tf.fau.de\/?p=1110","footnotes":""},"categories":[122,127,123],"tags":[67,65,75,66,74,64],"workflow_usergroup":[],"class_list":["post-1112","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-general","category-publication","category-research","tag-cahn-hilliard-navier-stokes","tag-filling-problems","tag-metal-casting","tag-mold-filling","tag-neural-operator--fourier-neural-operator","tag-surrogate-modeling","en-GB"],"_links":{"self":[{"href":"https:\/\/www.lgt.tf.fau.de\/wp-json\/wp\/v2\/posts\/1112","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.lgt.tf.fau.de\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.lgt.tf.fau.de\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.lgt.tf.fau.de\/wp-json\/wp\/v2\/users\/886"}],"replies":[{"embeddable":true,"href":"https:\/\/www.lgt.tf.fau.de\/wp-json\/wp\/v2\/comments?post=1112"}],"version-history":[{"count":10,"href":"https:\/\/www.lgt.tf.fau.de\/wp-json\/wp\/v2\/posts\/1112\/revisions"}],"predecessor-version":[{"id":1153,"href":"https:\/\/www.lgt.tf.fau.de\/wp-json\/wp\/v2\/posts\/1112\/revisions\/1153"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.lgt.tf.fau.de\/wp-json\/wp\/v2\/media\/1402"}],"wp:attachment":[{"href":"https:\/\/www.lgt.tf.fau.de\/wp-json\/wp\/v2\/media?parent=1112"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.lgt.tf.fau.de\/wp-json\/wp\/v2\/categories?post=1112"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.lgt.tf.fau.de\/wp-json\/wp\/v2\/tags?post=1112"},{"taxonomy":"workflow_usergroup","embeddable":true,"href":"https:\/\/www.lgt.tf.fau.de\/wp-json\/wp\/v2\/workflow_usergroup?post=1112"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}