物理
混合器
人工神经网络
编码器
标量(数学)
微流控
标量场
统计物理学
机械
经典力学
人工智能
计算机科学
热力学
几何学
数学
操作系统
作者
N. P. Chang,Ying Huai,Tingting Liu,Xi Chen,Yuqi Jin
出处
期刊:Physics of Fluids
[American Institute of Physics]
日期:2023-10-01
卷期号:35 (10)
被引量:4
摘要
Electro-osmotic micromixers (EMMs) are used for manipulating microfluidics because of the advantages on electro-osmosis mechanisms. The intricate interdependence between various fields in the EMM model presents a challenge for traditional numerical methods. In this paper, the flow parameters and electric potential are predicted based on the solute concentration by utilizing the physics-informed neural networks (PINNs) method. The unknown spatiotemporal dependent fields are derived from a deep neural network trained by minimizing the loss function integrating data of scalar field and corresponding governing equations. Moreover, the auto-encoder structure is developed to improve the performance of PINNs in the EMM. The comparisons between the results of auto-encoder PINNs and previous PINNs show a reduction in relative errors for transverse and longitudinal velocities from 83.35% and 84.24% to 9.88% and 12.29%, respectively, in regions with large-gradient velocities. Furthermore, our results demonstrate that the proposed method is robust to noise in the scalar concentration.
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