此后,Sirovich(1987)介绍了用于计算POD的snapshot方法,Berkooz(1993)详细的介绍了POD,Hall(2000年)给出了POD方法构造ROM(reduced-order models,降阶模型)的应用实例(二维翼型降阶模型计算)。 POD与数据处理中的PCA(principal components analysis,主成分分析)、信号处理中的离散K-L变换(discrete Karhunen–Loève tran...
Data-driven modelsare based on data. Machine and statistical learning algorithms are used for building such models from data. For this, data need to be explored, usually several models are considered, and finally a model is built through the application of a particular algorithm. This model will...
In other words, it models the belonging of the firm or of the scientist to different groups implicitly defined by shared practices and behaviors. Such a membership attribute is in agreement with the analysis of real-world networks reported by [5, 23]. The agent’s dynamics can be divided ...
(2) the above models assume that wind is uniform and steady-flow, whereas the wind flow at the bridge site may be nonuniform in the spanwise direction and unsteady during a VIV event; (3) the Reynolds number of a section model is generally one to two orders of magnitude smaller than ...
Before developing data-driven models, a set of input features derived from meteorological factors were constructed. After that, the data-driven models linking these selected input features and the PM10concentration using two machine-learning algorithms, namely, MLR and ANN were developed. Finally, base...
perspectives. Based on environmental factors and crop growth data, the growth processes of crops and their responses to the environment can be simulated quantitatively. The basic principles of crop growth models are illustrated, and applications of data-driven crop modeling in plant factories are ...
The direct generalization of data dependencies is a critical step in building data-driven models. DOI: 10.1007/978-3-540-85081-6_5 被引量: 4 年份: 2008 收藏 引用 批量引用 报错 分享 全部来源 求助全文 Springer adsabs.harvard.edu ResearchGate mendeley.com 相似文献 参考文献 引证文献Dynamic ...
(EBPNN)-based discharge forecasting andK-nearest neighbor algorithm-based discharge error forecasting. This model is proposed for solving the hard problem of how to implement non-updating rainfall–runoff simulation by data-driven models. For the purpose of solving the hard problems, the PBK model...
Recent research in inverse problems seeks to develop a mathematically coherent foundation for combining data-driven models, and in particular those based on deep learning, with domain-specific knowledge contained in physical–analytical models. The focus is on solving ill-posed inverse problems that are...
complete description. The second method relies on Bayesian inference, which can be used to calibrate models to give data and quantification of the model uncertainty. The method is applied to calibrate parameters in thermodynamic models of the Gibbs energy of Ti-W alloys. The uncertainty of the ...