[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4911":3,"related-4911":72},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":18,"tags":20,"primary_topic":9,"secondary_topics":21,"classification":22,"search_phrases":45,"slug":46,"view_count":15,"doi":47,"paper":48,"created_at":71},4911,"Mathematical Foundation of Digital Twins","https:\u002F\u002Fdoi.org\u002F10.4171\u002Fowr\u002F2026\u002F26","A digital twin (DT) is a computational model that evolves over time to persistently represent the structure, behavior, and context of a unique physical system or process. DTs are characterized by a dynamic and continuous two-way flow of information between the computational model and the physical system. Data streams from the physical system are assimilated into the computational model to reduce uncertainties and improve predictions of the model, which in turn is used as a basis for controlling the physical system, optimizing data acquisition, and providing decision support. The DT paradigm brings along challenging mathematical and statistical questions which were at the center of the workshop.","数字孪生(DT)是一种随时间演化的计算模型，持续地表示一个独特物理系统或过程的结构、行为和情境。数字孪生的特征在于计算模型与物理系统之间动态且持续的双向信息流。来自物理系统的数据流被同化到计算模型中，以降低不确定性并改进模型的预测，而模型反过来又被用作控制物理系统、优化数据采集和提供决策支持的基础。数字孪生范式带来了一系列具有挑战性的数学和统计问题，这些问题正是本次研讨会的核心议题。",null,"Oberwolfach Reports","2026-10-12T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"数字孪生数学基础的理论研讨会报告，与三农、农业信息化、智慧农业无直接关联，不建议进入每日精选。",[19],{"name":10,"url":6},[12],[],{"status":23,"locked":14,"version":24,"method":25,"evidence":26},"needs_review","2026-10-22.2","rules",{"candidates":27,"agricultural_evidence":14,"reason":44},[28,37],{"code":29,"score":30,"supported":31,"title":32,"abstract":33,"tags":36},"I1",6,true,[],[34,35],"数字孪生","digital twin",[],{"code":38,"score":39,"supported":31,"title":40,"abstract":41,"tags":43},"I2",3,[],[42],"decision support",[],"农业相关性需要复核",[],"MathematicalFoundationofDigitalTwins-4911","10.4171\u002Fowr\u002F2026\u002F26",{"doi":47,"openalex_id":49,"authors":50,"venue":10,"cited_by_count":15,"oa_url":63,"card":64,"direction":68,"ingested_from":70},"W7221555175",[51,54,57,60],{"name":52,"orcid":53},"Omar N. Ghattas","https:\u002F\u002Forcid.org\u002F0000-0001-7742-2509",{"name":55,"orcid":56},"Matthias Heinkenschloss B","https:\u002F\u002Forcid.org\u002F0000-0002-9305-4221",{"name":58,"orcid":59},"Sebastian Reich","https:\u002F\u002Forcid.org\u002F0000-0002-5336-8904",{"name":61,"orcid":62},"Claudia Schillings","https:\u002F\u002Forcid.org\u002F0009-0003-7121-2457","https:\u002F\u002Fems.press\u002Fcontent\u002Fserial-article-files\u002F54367",{"tldr":65,"method":66,"finding":67,"direction":68,"opportunity":69},"该论文探讨数字孪生的数学基础，聚焦模型与物理系统间双向信息流中的数学与统计挑战。","基于研讨会成果，梳理数字孪生中数据同化、不确定性量化与优化控制的数学框架。","数字孪生的核心数学挑战在于动态双向信息流下的模型不确定性降低与决策支持。","农业人工智能与决策模型","将数字孪生数学框架应用于农业系统，研究作物生长模型与田间数据实时同化的不确定性量化方法。","openalex","2026-10-14T23:30:49.791227Z",{"total":15,"page":73,"page_size":73,"items":74},1,[]]