[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"tag-co-kriging":3},{"tag":4,"articles":9},{"id":5,"name":6,"slug":6,"article_count":7,"description_zh":8,"description_en":8},"042780f6-da68-4d91-819c-7bd618311529","co-kriging",0,null,[10],{"id":11,"slug":12,"title":13,"summary":14,"category":15,"image_url":16,"cover_image":16,"language":17,"created_at":18},"f9e6f569-9b4d-40cd-8e96-849e5ff915a8","multi-fidelity-models-composite-mechanics-zh","複材力學的多保真模型怎麼省算力","這篇綜述整理 co-kriging、深度高斯過程與多保真神經網路，說明如何用低成本資料搭配少量高精度資料，降低複合材料力學預測的計算與實驗成本。","research","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1777962662245-tz1x.png","zh","2026-05-05T06:30:39.254854+00:00"]