典型文献
Data-driven modeling of a four-dimensional stochastic projectile system
文献摘要：
The dynamical modeling of projectile systems with sufficient accuracy is of great difficulty due to high-dimensional space and various perturbations.With the rapid development of data science and scientific tools of measurement recently,there are numerous data-driven methods devoted to discovering governing laws from data.In this work,a data-driven method is employed to perform the modeling of the projectile based on the Kramers-Moyal formulas.More specifically,the four-dimensional projectile system is assumed as an It? stochastic differential equation.Then the least square method and sparse learning are applied to identify the drift coefficient and diffusion matrix from sample path data,which agree well with the real system.The effectiveness of the data-driven method demonstrates that it will become a powerful tool in extracting governing equations and predicting complex dynamical behaviors of the projectile.
文献关键词：
中图分类号：
作者姓名：
Yong Huang;Yang Li
作者机构：
School of Energy and Power Engineering,Nanjing University of Science and Technology,Nanjing 210094,China;School of Automation,Nanjing University of Science and Technology,Nanjing 210094,China
文献出处：
引用格式：
[1]Yong Huang;Yang Li-.Data-driven modeling of a four-dimensional stochastic projectile system)[J].中国物理B（英文版）,2022(07):179-184
A类：
Moyal
B类：
Data,driven,modeling,four,dimensional,stochastic,projectile,dynamical,systems,sufficient,accuracy,great,difficulty,due,high,space,various,perturbations,With,rapid,development,data,science,scientific,tools,measurement,recently,there,numerous,methods,devoted,discovering,governing,laws,from,In,this,work,employed,perform,Kramers,formulas,More,specifically,assumed,It,differential,Then,least,square,sparse,learning,applied,identify,drift,coefficient,diffusion,matrix,sample,path,which,agree,well,real,effectiveness,demonstrates,that,will,become,powerful,extracting,equations,predicting,complex,behaviors
AB值：
0.58624
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