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
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%。
