Citation

BibTex format

@article{Zhuang:2022:10.1039/d2lc00303a,
author = {Zhuang, Y and Cheng, S and Kovalchuk, N and Simmons, M and Matar, OK and Guo, Y-K and Arcucci, R},
doi = {10.1039/d2lc00303a},
journal = {Lab on a Chip: miniaturisation for chemistry, physics, biology, materials science and bioengineering},
pages = {3187--3202},
title = {Ensemble latent assimilation with deep learning surrogate model: application to drop interaction in a microfluidics device},
url = {http://dx.doi.org/10.1039/d2lc00303a},
volume = {22},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - A major challenge in the field of microfluidics is to predict and control drop interactions. This work develops an image-based data-driven model to forecast drop dynamics based on experiments performed on a microfluidics device. Reduced-order modelling techniques are applied to compress the recorded images into low-dimensional spaces and alleviate the computational cost. Recurrent neural networks are then employed to build a surrogate model of drop interactions by learning the dynamics of compressed variables in the reduced-order space. The surrogate model is integrated with real-time observations using data assimilation. In this paper we developed an ensemble-based latent assimilation algorithm scheme which shows an improvement in terms of accuracy with respect to the previous approaches. This work demonstrates the possibility to create a reliable data-driven model enabling a high fidelity prediction of drop interactions in microfluidics device. The performance of the developed system is evaluated against experimental data (i.e., recorded videos), which are excluded from the training of the surrogate model. The developed scheme is general and can be applied to other dynamical systems.
AU  - Zhuang,Y
AU  - Cheng,S
AU  - Kovalchuk,N
AU  - Simmons,M
AU  - Matar,OK
AU  - Guo,Y-K
AU  - Arcucci,R
DO  - 10.1039/d2lc00303a
EP  - 3202
PY  - 2022///
SN  - 1473-0189
SP  - 3187
TI  - Ensemble latent assimilation with deep learning surrogate model: application to drop interaction in a microfluidics device
T2  - Lab on a Chip: miniaturisation for chemistry, physics, biology, materials science and bioengineering
UR  - http://dx.doi.org/10.1039/d2lc00303a
UR  - https://www.ncbi.nlm.nih.gov/pubmed/35875987
UR  - http://hdl.handle.net/10044/1/98534
VL  - 22
ER  -
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