Sparse wavefield reconstruction based on Physics-Informed neural networks

doi:10.1016/j.ultras.2025.107582

IMR OpenIR

	Sparse wavefield reconstruction based on Physics-Informed neural networks
	Xu, Bin 1; Zou, Yun 1; Sha, Gaofeng 2; Yang, Liang 3; Cai, Guixi 3; Li, Yang 1
通讯作者	Li, Yang(yangli@zzu.edu.cn)
	2025-05-01
发表期刊	ULTRASONICS
ISSN	0041-624X
卷号	149 页码:12
摘要	In recent years, the widespread application of laser ultrasonic (LU) devices for obtaining internal material information has been observed. However, this approach demands a significant amount of time to acquire complete wavefield data. Hence, there is a necessity to reduce the acquisition time. In this work, we propose a method based on physics-informed neural networks to decrease the required sampling measurements. We utilize sparse sampling of full experimental data as input data to reconstruct complete wavefield data. Specifically, we employ physics-informed neural networks to learn the propagation characteristics from the sparsely sampled data and partition the complete grid to reconstruct the full wavefield. We achieved 95% reconstruction accuracy using four hundredth of the total measurements. The proposed method can be utilized not only for sparse wavefield reconstruction in LU testing but also for other wavefield reconstructions, such as those required in online monitoring systems.
关键词	Physics-Informed Neural Networks Wavefield Reconstruction Laser Ultrasonic Non-destructive Testing
资助者	National Natural Science Founda-tion of China ; Henan Provincial Key Scientific Research Project of Higher Education Institutions
DOI	10.1016/j.ultras.2025.107582
收录类别	SCI
语种	英语
资助项目	National Natural Science Founda-tion of China[51705470] ; Henan Provincial Key Scientific Research Project of Higher Education Institutions[222102220025]
WOS研究方向	Acoustics ; Radiology, Nuclear Medicine & Medical Imaging
WOS类目	Acoustics ; Radiology, Nuclear Medicine & Medical Imaging
WOS记录号	WOS:001409822300001
出版者	ELSEVIER
引用统计
文献类型	期刊论文
条目标识符	http://ir.imr.ac.cn/handle/321006/180659
专题	中国科学院金属研究所
通讯作者	Li, Yang
作者单位	1.Zhengzhou Univ, Sch Mech & Power Engn, Zhengzhou 450001, Peoples R China 2.Clover Pk Tech Coll, Sch Adv Mfg, Lakewood, WA 98499 USA 3.Chinese Acad Sci, Inst Met Res, Shenyang 110016, Peoples R China
推荐引用方式 GB/T 7714	Xu, Bin,Zou, Yun,Sha, Gaofeng,et al. Sparse wavefield reconstruction based on Physics-Informed neural networks[J]. ULTRASONICS,2025,149:12.
APA	Xu, Bin,Zou, Yun,Sha, Gaofeng,Yang, Liang,Cai, Guixi,&Li, Yang.(2025).Sparse wavefield reconstruction based on Physics-Informed neural networks.ULTRASONICS,149,12.
MLA	Xu, Bin,et al."Sparse wavefield reconstruction based on Physics-Informed neural networks".ULTRASONICS 149(2025):12.