Journal
LAB ON A CHIP
Volume 22, Issue 17, Pages 3187-3202Publisher
ROYAL SOC CHEMISTRY
DOI: 10.1039/d2lc00303a
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Funding
- Leverhulme Centre for Wildfires, Environment and Society through the Leverhulme Trust [RC-2018-023]
- [EP/T000414/1]
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This study develops an image-based data-driven model to predict the dynamics of drop interactions in microfluidics device. Reduced-order modelling techniques and recurrent neural networks are used to build a surrogate model by learning the dynamics of compressed variables in low-dimensional space. The developed model, integrated with real-time observations using data assimilation, shows an improvement in accuracy and can be applied to other dynamical systems.
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.
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