Deep learning for imaging-free voids detection by ultrasonic data: bridging numerical data and model experiments
- DOI
- 10.1016/j.ultras.2026.108268
- Published
- 2027-01
- Container
- Ultrasonics
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.ultras.2026.108268,
title = {Deep learning for imaging-free voids detection by ultrasonic data: bridging numerical data and model experiments},
author = {Shujie Chen and Zhenming Shi and Liu Liu and Ming Peng and Qiyu Wu},
year = {2027},
journal = {Ultrasonics},
doi = {10.1016/j.ultras.2026.108268},
url = {https://doi.org/10.1016/j.ultras.2026.108268}
}RIS
TY - JOUR TI - Deep learning for imaging-free voids detection by ultrasonic data: bridging numerical data and model experiments AU - Shujie Chen AU - Zhenming Shi AU - Liu Liu AU - Ming Peng AU - Qiyu Wu PY - 2027 JO - Ultrasonics DO - 10.1016/j.ultras.2026.108268 UR - https://doi.org/10.1016/j.ultras.2026.108268 ER -
APA
Chen, S., Shi, Z., Liu, L., Peng, M., & Wu, Q. (2027). Deep learning for imaging-free voids detection by ultrasonic data: bridging numerical data and model experiments. Ultrasonics. https://doi.org/10.1016/j.ultras.2026.108268
Source records
- crossref · retrieved 2026-09-26T23:18:20.520Z