GAT-based Concentration Prediction for Random Microfluidic Mixers with Multiple Input Flow Rates
Pages 483 - 488
Abstract
Microfluidic biochips have emerged with significant promise and versatility in automating a variety of biochemical protocols. Accurate preparation of fluid samples with microfluidic mixers is an essential component of these protocols, where concentration prediction and generation are critical. Recently, machine learning models have been adopted in concentration prediction, which demonstrate great potential in enhancing the efficiency and scalability over the traditional finite element analysis (FEA) methods. However, the state-of-the-art machine learning-based method can only predict the concentration of microfluidic mixers with fixed input flow rates, but suffers poor prediction accuracy for multiple input flow rates. To address this issue, this paper proposes a new concentration prediction method based on the graph attention networks (GAT). By modeling each channel of the mixer as a graph node in a GAT, the proposed method efficiently and accurately predicts the generated concentration of random microfluidic mixers with multiple input flow rates. Experimental results show that compared with the state-of-the-art method, the proposed GAT-based simulation method obtains a reduction of 85% in terms of errors of predicted concentration, which validates the effectiveness of the proposed GAT model.
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Index Terms
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June 2023
731 pages
ISBN:9798400701252
DOI:10.1145/3583781
- General Chairs:
- Himanshu Thapliyal,
- Ronald DeMara,
- Program Chairs:
- Inna Partin-Vaisband,
- Srinivas Katkoori
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Published: 05 June 2023
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- the Key Program of National Natural Science Foundation of China
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