Ensemble latent assimilation with deep learning surrogate model: application to drop interaction in a microfluidics device,Lab on a Chip

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Ensemble latent assimilation with deep learning surrogate model: application to drop interaction in a microfluidics device
Lab on a Chip ( IF 6.1 ) Pub Date : 2022-07-05 , DOI: 10.1039/d2lc00303a
Yilin Zhuang ₁ , Sibo Cheng ₂ , Nina Kovalchuk ₃ , Mark Simmons ₃ , Omar K Matar ₁ , Yi-Ke Guo ₂ , Rossella Arcucci _{2,

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Affiliation

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.

中文翻译：

使用深度学习代理模型进行集成潜在同化：在微流体设备中滴下交互的应用

微流体领域的一个主要挑战是预测和控制液滴相互作用。这项工作开发了一种基于图像的数据驱动模型，以根据在微流体设备上进行的实验来预测液滴动力学。降阶建模技术用于将记录的图像压缩到低维空间并降低计算成本。然后使用递归神经网络通过学习降阶空间中压缩变量的动力学来构建液滴交互的代理模型。代理模型与使用数据同化的实时观测相结合。在本文中，我们开发了一种基于集成的潜在同化算法方案，该方案相对于以前的方法在准确性方面有所提高。这项工作证明了创建可靠的数据驱动模型的可能性，该模型能够对微流体设备中的液滴相互作用进行高保真预测。根据实验数据评估开发系统的性能（即，录制的视频），它们被排除在代理模型的训练之外。所开发的方案是通用的，可以应用于其他动力系统。

更新日期：2022-07-05

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