Naiyin Zhang

Predicting the fluid behavior of random microfluidic mixers using convolutional neural networks† Junchao Wang, ‡*a Naiyin Zhang, ‡b Jinkai Chen, a Guodong Su,a Hailong Yao,c Tsung-Yi Hod and Lingling Suna Author affiliations * Corresponding authors a Key Laboratory of RF Circuits and Systems, Ministry of Education, and, Zhejiang Provincial Laboratory of Integrated Circuit Design, Hangzhou Dianzi University, China E-mail: junchao@hdu.edu.cn Tel: +86 571 8691 9078 b School of Artificial...

Predicting the fluid behavior of random microfluidic mixers using convolutional neural networks Junchao Wang, Naiyin Zhang, Jinkai Chen, Guodong Su, Hailong Yao, Tsung-Yi Ho and LingLing Sun Abstract With the various applications of microfluidics, numerical simulation is highly recommended to verify its performance and reveal potential defects before fabrication.

Finding the optimal design of a passive microfluidic mixer The ability to thoroughly mix two fluids is a fundamental need in microfluidics. While a variety of different microfluidic mixers have been designed by researchers, it remains unknown which (if any) of these mixers are optimal (that is, which designs provide the most thorough mixing with the smallest possible fluidic resistance across the mixer). In this work, we automatically designed and rationally optimized a microfluidic mixer.