As pivotal supporting technologies for smart manufacturing and digital engineering,model-based and data-driven methods have been widely applied in many industrial fields,such as product design,process monitoring,and s...As pivotal supporting technologies for smart manufacturing and digital engineering,model-based and data-driven methods have been widely applied in many industrial fields,such as product design,process monitoring,and smart maintenance.While promising,both methods have issues that need to be addressed.For example,model-based methods are limited by low computational accuracy and a high computational burden,and data-driven methods always suffer from poor interpretability and redundant features.To address these issues,the concept of data-model fusion(DMF)emerges as a promising solution.DMF involves integrating model-based methods with data-driven methods by incorporating big data into model-based methods or embedding relevant domain knowledge into data-driven methods.Despite growing efforts in the field of DMF,a unanimous definition of DMF remains elusive,and a general framework of DMF has been rarely discussed.This paper aims to address this gap by providing a thorough overview and categorization of both data-driven methods and model-based methods.Subsequently,this paper also presents the definition and categorization of DMF and discusses the general framework of DMF.Moreover,the primary seven applications of DMF are reviewed within the context of smart manufacturing and digital engineering.Finally,this paper directs the future directions of DMF.展开更多
This paper presents a method for measuring stress fields within the framework of coupled data models,aimed at determining stress fields in isotropic material structures exhibiting localized deterioration behavior with...This paper presents a method for measuring stress fields within the framework of coupled data models,aimed at determining stress fields in isotropic material structures exhibiting localized deterioration behavior without relying on constitutive equations in the deteriorated region.This approach contributes to advancing the field of intrinsic equation-free mechanics.The methodology combines measured strain fields with data-model coupling driven algorithms.The gradient and Canny operators are utilized to process the strain field data,enabling the determination of the deterioration region's location.Meanwhile,an adaptive model building method is proposed for constructing coupling driven models.To address the issue of unknown datasets during computation,a dataset updating strategy based on a differential evolutionary algorithm is introduced.The resulting optimal dataset is then used to generate stress field results.Validation against finite element method calculations demonstrates the accuracy of the proposed method in obtaining full-field stresses in specimens with local degradation behavior.展开更多
现有GNSS水汽层析研究主要聚焦于如何提升卫星观测数据利用率,但在卫星信号数据优选方面研究较少,导致穿过同一组网格集的层析观测方程线性近似且方程系数矩阵列向量元素多数为零,水汽层析模型病态严重。针对该现状,本文提出一种GNSS卫...现有GNSS水汽层析研究主要聚焦于如何提升卫星观测数据利用率,但在卫星信号数据优选方面研究较少,导致穿过同一组网格集的层析观测方程线性近似且方程系数矩阵列向量元素多数为零,水汽层析模型病态严重。针对该现状,本文提出一种GNSS卫星信号自适应优选的水汽层析方法,解决层析模型设计矩阵零元素较多和层析模型病态的难题。该方法基于网格覆盖率最大原则确定层析区域水平网格划分,并发展联合卫星高度角与方位角阈值的卫星信号自适应优选方法,克服水汽层析模型观测方程线性近似的难题。本文选取香港地区2013年5月2日—2013年5月7日共6 d 12个GNSS测站及1个无线电探空站数据为例进行试验。与现有方法相比,本文方法能在降低卫星信号利用率的同时保证网格覆盖率,克服相似卫星信号造成层析模型设计矩阵病态的现状。以无线电探空数据为真值,发现本文方法反演水汽密度廓线的平均RMS、MAE和Bias分别为1.03、0.80和0.13 g/m^(3),优于传统方法的1.25、0.97和0.10 g/m^(3),其RMS改善率为20.78%;此外,本文方法在模型解算效率方面也优于传统方法,其模型计算效率平均提升9.51%。展开更多
图结构数据在社交网络、交通系统、生物信息等场景中广泛存在。图神经网络(graph neural networks,GNNs)利用消息传递机制迭代地聚合邻居信息,在节点分类、链路预测和图分类等任务中展现出良好性能。然而,随着数据规模的持续扩大与应用...图结构数据在社交网络、交通系统、生物信息等场景中广泛存在。图神经网络(graph neural networks,GNNs)利用消息传递机制迭代地聚合邻居信息,在节点分类、链路预测和图分类等任务中展现出良好性能。然而,随着数据规模的持续扩大与应用场景的日趋复杂,GNNs面临表达能力有限与泛化能力不足等关键挑战。近年来,以大语言模型(large language models,LLMs)为代表的基础模型迅速发展,展现出卓越的泛化与推理能力,为图机器学习领域带来了新的启发。基于此,本研究提出图基础模型(graph foundation model,GFM)的概念,希望通过在大规模图数据上预训练,获得能够灵活适配多种下游任务的通用模型;同时系统梳理了近年来图基础模型的相关研究,并依据其对GNNs与LLMs的依赖程度,将现有方法归纳为3类,综述其研究进展并介绍了作者团队在相关方向的实践探索经验。最后,展望了图基础模型未来发展可能面临的关键挑战与前景,以期为图机器学习领域的持续创新提供参考。展开更多
The response of the agro-ecological system to the environment includes the response of individual crop＇s physiologic process and the adaption of the crop commu- nity to the environment. Observation and simulation at ...The response of the agro-ecological system to the environment includes the response of individual crop＇s physiologic process and the adaption of the crop commu- nity to the environment. Observation and simulation at the single scale level cannot fully explain the above process. It is necessary to develop cross-scale agro-ecological models and study the interaction of agro-ecological processes across different scales. In this research, two typical agro- ecological models, the Decision Support System for Agro- technology Transfer （DSSAT） model and the Agro- ecological Zone （AEZ） model, are employed, and a framework for effective cross-scale data-model fusion is proposed and illustrated. The national observed data from 36 different agricultural observation stations and historical weather stations （1962-1999） are employed to estimate average crop productivity. Comparison of the two models＇ estimations are consistent, which would indicate the possibility ofcross-scale crop model fusion.展开更多
基金supported in part by the National Natural Science Foundation of China(NSFC)under Grants(52275471 and 52120105008)the Beijing Outstanding Young Scientist Program,and the New Cornerstone Science Foundation through the XPLORER PRIZE.
文摘As pivotal supporting technologies for smart manufacturing and digital engineering,model-based and data-driven methods have been widely applied in many industrial fields,such as product design,process monitoring,and smart maintenance.While promising,both methods have issues that need to be addressed.For example,model-based methods are limited by low computational accuracy and a high computational burden,and data-driven methods always suffer from poor interpretability and redundant features.To address these issues,the concept of data-model fusion(DMF)emerges as a promising solution.DMF involves integrating model-based methods with data-driven methods by incorporating big data into model-based methods or embedding relevant domain knowledge into data-driven methods.Despite growing efforts in the field of DMF,a unanimous definition of DMF remains elusive,and a general framework of DMF has been rarely discussed.This paper aims to address this gap by providing a thorough overview and categorization of both data-driven methods and model-based methods.Subsequently,this paper also presents the definition and categorization of DMF and discusses the general framework of DMF.Moreover,the primary seven applications of DMF are reviewed within the context of smart manufacturing and digital engineering.Finally,this paper directs the future directions of DMF.
基金supported by the Fundamental Research Fund for the Central Universities(Grant No.BLX202226)。
文摘This paper presents a method for measuring stress fields within the framework of coupled data models,aimed at determining stress fields in isotropic material structures exhibiting localized deterioration behavior without relying on constitutive equations in the deteriorated region.This approach contributes to advancing the field of intrinsic equation-free mechanics.The methodology combines measured strain fields with data-model coupling driven algorithms.The gradient and Canny operators are utilized to process the strain field data,enabling the determination of the deterioration region's location.Meanwhile,an adaptive model building method is proposed for constructing coupling driven models.To address the issue of unknown datasets during computation,a dataset updating strategy based on a differential evolutionary algorithm is introduced.The resulting optimal dataset is then used to generate stress field results.Validation against finite element method calculations demonstrates the accuracy of the proposed method in obtaining full-field stresses in specimens with local degradation behavior.
文摘现有GNSS水汽层析研究主要聚焦于如何提升卫星观测数据利用率,但在卫星信号数据优选方面研究较少,导致穿过同一组网格集的层析观测方程线性近似且方程系数矩阵列向量元素多数为零,水汽层析模型病态严重。针对该现状,本文提出一种GNSS卫星信号自适应优选的水汽层析方法,解决层析模型设计矩阵零元素较多和层析模型病态的难题。该方法基于网格覆盖率最大原则确定层析区域水平网格划分,并发展联合卫星高度角与方位角阈值的卫星信号自适应优选方法,克服水汽层析模型观测方程线性近似的难题。本文选取香港地区2013年5月2日—2013年5月7日共6 d 12个GNSS测站及1个无线电探空站数据为例进行试验。与现有方法相比,本文方法能在降低卫星信号利用率的同时保证网格覆盖率,克服相似卫星信号造成层析模型设计矩阵病态的现状。以无线电探空数据为真值,发现本文方法反演水汽密度廓线的平均RMS、MAE和Bias分别为1.03、0.80和0.13 g/m^(3),优于传统方法的1.25、0.97和0.10 g/m^(3),其RMS改善率为20.78%;此外,本文方法在模型解算效率方面也优于传统方法,其模型计算效率平均提升9.51%。
文摘图结构数据在社交网络、交通系统、生物信息等场景中广泛存在。图神经网络(graph neural networks,GNNs)利用消息传递机制迭代地聚合邻居信息,在节点分类、链路预测和图分类等任务中展现出良好性能。然而,随着数据规模的持续扩大与应用场景的日趋复杂,GNNs面临表达能力有限与泛化能力不足等关键挑战。近年来,以大语言模型(large language models,LLMs)为代表的基础模型迅速发展,展现出卓越的泛化与推理能力,为图机器学习领域带来了新的启发。基于此,本研究提出图基础模型(graph foundation model,GFM)的概念,希望通过在大规模图数据上预训练,获得能够灵活适配多种下游任务的通用模型;同时系统梳理了近年来图基础模型的相关研究,并依据其对GNNs与LLMs的依赖程度,将现有方法归纳为3类,综述其研究进展并介绍了作者团队在相关方向的实践探索经验。最后,展望了图基础模型未来发展可能面临的关键挑战与前景,以期为图机器学习领域的持续创新提供参考。
文摘The response of the agro-ecological system to the environment includes the response of individual crop＇s physiologic process and the adaption of the crop commu- nity to the environment. Observation and simulation at the single scale level cannot fully explain the above process. It is necessary to develop cross-scale agro-ecological models and study the interaction of agro-ecological processes across different scales. In this research, two typical agro- ecological models, the Decision Support System for Agro- technology Transfer （DSSAT） model and the Agro- ecological Zone （AEZ） model, are employed, and a framework for effective cross-scale data-model fusion is proposed and illustrated. The national observed data from 36 different agricultural observation stations and historical weather stations （1962-1999） are employed to estimate average crop productivity. Comparison of the two models＇ estimations are consistent, which would indicate the possibility ofcross-scale crop model fusion.
