A large amount of data can partly assure good fitting quality for the trained neural networks.When the quantity of experimental or on-site monitoring data is commonly insufficient and the quality is difficult to contr...A large amount of data can partly assure good fitting quality for the trained neural networks.When the quantity of experimental or on-site monitoring data is commonly insufficient and the quality is difficult to control in engineering practice,numerical simulations can provide a large amount of controlled high quality data.Once the neural networks are trained by such data,they can be used for predicting the properties/responses of the engineering objects instantly,saving the further computing efforts of simulation tools.Correspondingly,a strategy for efficiently transferring the input and output data used and obtained in numerical simulations to neural networks is desirable for engineers and programmers.In this work,we proposed a simple image representation strategy of numerical simulations,where the input and output data are all represented by images.The temporal and spatial information is kept and the data are greatly compressed.In addition,the results are readable for not only computers but also human resources.Some examples are given,indicating the effectiveness of the proposed strategy.展开更多
基金support from the National Natural Science Foundation of China(NSFC)(52178324).
文摘A large amount of data can partly assure good fitting quality for the trained neural networks.When the quantity of experimental or on-site monitoring data is commonly insufficient and the quality is difficult to control in engineering practice,numerical simulations can provide a large amount of controlled high quality data.Once the neural networks are trained by such data,they can be used for predicting the properties/responses of the engineering objects instantly,saving the further computing efforts of simulation tools.Correspondingly,a strategy for efficiently transferring the input and output data used and obtained in numerical simulations to neural networks is desirable for engineers and programmers.In this work,we proposed a simple image representation strategy of numerical simulations,where the input and output data are all represented by images.The temporal and spatial information is kept and the data are greatly compressed.In addition,the results are readable for not only computers but also human resources.Some examples are given,indicating the effectiveness of the proposed strategy.
文摘为减小建模工作量,降低数值模拟的门槛,利用Matlab软件开发区域地面沉降数值模拟可视化系统,用于高效建立数值模型,预测抽取地下水引起的区域地面沉降量.系统共有数据分析、模型管理、网格剖分、前处理、后处理及模型求解六个主模块.数据分析模块用于管理并分析钻孔、水位、分层标三种时空数据,初步计算模型参数.网格剖分、前处理和后处理模块用于建立并运行模型,校正参数,输出结果可视化,进行模型不确定性分析.运用可视化系统,能够快速建立、校正上海市地面沉降数值模型.上海市地面沉降模型预测结果显示2012-2022年间上海地面将回弹抬升0-30 mm.
