近年来,洪涝灾害频发,给社会带来严重影响,而洪涝灾害期间往往伴随着显著的河流水位变化和大气可降水量(precipitable water vapor,PWV)变化.本文以2024年发生在巴西阿雷格里港的洪涝灾害为例,选取GNSS站观测数据,分别开展了洪涝水位和...近年来,洪涝灾害频发,给社会带来严重影响,而洪涝灾害期间往往伴随着显著的河流水位变化和大气可降水量(precipitable water vapor,PWV)变化.本文以2024年发生在巴西阿雷格里港的洪涝灾害为例,选取GNSS站观测数据,分别开展了洪涝水位和PWV监测研究.结果表明,暴雨前SPH4站水位反演与水文站数据的相关系数为0.993,均方根误差(root mean square error,RMSE)为0.02 m;暴雨期间,河流两岸的SPH4站与IDP1站的水位反演结果相关系数达到0.997,RMSE为0.06 m,降雨峰值与水位峰值存在2~5 d不等的时间差.GNSS站反演的PWV与探空站实测PWV的相关系数为0.992,RMSE仅为1.9 mm,PWV值达到峰值的5 h内出现降雨最大值.实验证明,岸基GNSS设备能够准确反演出洪涝水位变化和PWV变化,在洪涝灾害的预防和监测方面具有广阔的应用前景.展开更多
Finding materials with specific properties is a hot topic in materials science.Traditional materials design relies on empirical and trial-and-error methods,requiring extensive experiments and time,resulting in high co...Finding materials with specific properties is a hot topic in materials science.Traditional materials design relies on empirical and trial-and-error methods,requiring extensive experiments and time,resulting in high costs.With the development of physics,statistics,computer science,and other fields,machine learning offers opportunities for systematically discovering new materials.Especially through machine learning-based inverse design,machine learning algorithms analyze the mapping relationships between materials and their properties to find materials with desired properties.This paper first outlines the basic concepts of materials inverse design and the challenges faced by machine learning-based approaches to materials inverse design.Then,three main inverse design methods—exploration-based,model-based,and optimization-based—are analyzed in the context of different application scenarios.Finally,the applications of inverse design methods in alloys,optical materials,and acoustic materials are elaborated on,and the prospects for materials inverse design are discussed.The authors hope to accelerate the discovery of new materials and provide new possibilities for advancing materials science and innovative design methods.展开更多
基金funded by theNationalNatural Science Foundation of China(52061020)Major Science and Technology Projects in Yunnan Province(202302AG050009)Yunnan Fundamental Research Projects(202301AV070003).
文摘Finding materials with specific properties is a hot topic in materials science.Traditional materials design relies on empirical and trial-and-error methods,requiring extensive experiments and time,resulting in high costs.With the development of physics,statistics,computer science,and other fields,machine learning offers opportunities for systematically discovering new materials.Especially through machine learning-based inverse design,machine learning algorithms analyze the mapping relationships between materials and their properties to find materials with desired properties.This paper first outlines the basic concepts of materials inverse design and the challenges faced by machine learning-based approaches to materials inverse design.Then,three main inverse design methods—exploration-based,model-based,and optimization-based—are analyzed in the context of different application scenarios.Finally,the applications of inverse design methods in alloys,optical materials,and acoustic materials are elaborated on,and the prospects for materials inverse design are discussed.The authors hope to accelerate the discovery of new materials and provide new possibilities for advancing materials science and innovative design methods.
