Ensemble latent assimilation with deep learning surrogate model: application to drop interaction in a microfluidics device
- DOI
- 10.1039/d2lc00303a
- Published
- 2022
- Container
- Lab on a Chip
- Publisher
- Royal Society of Chemistry (RSC)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1039/d2lc00303a,
title = {Ensemble latent assimilation with deep learning surrogate model: application to drop interaction in a microfluidics device},
author = {Yilin Zhuang and Sibo Cheng and Nina Kovalchuk and Mark Simmons and Omar K. Matar and Yi-Ke Guo and Rossella Arcucci},
year = {2022},
journal = {Lab on a Chip},
doi = {10.1039/d2lc00303a},
url = {https://doi.org/10.1039/d2lc00303a}
}RIS
TY - JOUR TI - Ensemble latent assimilation with deep learning surrogate model: application to drop interaction in a microfluidics device AU - Yilin Zhuang AU - Sibo Cheng AU - Nina Kovalchuk AU - Mark Simmons AU - Omar K. Matar AU - Yi-Ke Guo AU - Rossella Arcucci PY - 2022 JO - Lab on a Chip DO - 10.1039/d2lc00303a UR - https://doi.org/10.1039/d2lc00303a ER -
APA
Zhuang, Y., Cheng, S., Kovalchuk, N., Simmons, M., Matar, O. K., Guo, Y., & Arcucci, R. (2022). Ensemble latent assimilation with deep learning surrogate model: application to drop interaction in a microfluidics device. Lab on a Chip. https://doi.org/10.1039/d2lc00303a
Source records
- crossref · retrieved 2026-11-10T07:22:22.217Z