DATA-DRIVEN SPATIALLY DEPENDENT PDE IDENTIFICATION

被引:3
作者
Liu, Ruixian [1 ]
Bianco, Michael J. [1 ]
Gerstoft, Peter [1 ]
Rao, Bhaskar D. [1 ]
机构
[1] Univ Calif San Diego, La Jolla, CA 92093 USA
来源
2022 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) | 2022年
关键词
data-driven; l(1-)norm minimization; lasso; spatially-dependent PDEs; efficient identification; EQUATIONS;
D O I
10.1109/ICASSP43922.2022.9747392
中图分类号
O42 [声学];
学科分类号
070206 ; 082403 ;
摘要
We propose a data-driven partial differential equation (PDE) identification scheme based on l(1)-norm minimization which can identify spatially-dependent PDEs from measurements. Spatially-dependent PDEs refers to that the terms in the PDEs vary across space. In reality a physical system is often governed by spatially-dependent PDEs because the properties of the medium can be various across space, and the proposed method is the first data-driven spatially-dependent PDEs identification scheme. In addition, our method is efficient owing to its non-iterative nature and efficient implementation by coordinate descent.(1)
引用
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页码:3383 / 3387
页数:5
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