The rapid advancement of artificial intelligence is transforming the computer-aided design of microfluidic chips. As a key component, microfluidic mixers are widely used in bioengineering, chemical experiments, and medical diagnostics due to their efficient mixing capabilities. Traditionally, the simulation of these mixers relies on the finite element method (FEM), which, although effective, presents challenges due to its computational complexity and time-consuming nature. To address this, we propose a nine-grid network (NGN) model theory with a centrally symmetric structure.The NGN uses a symmetric structure similar to a 3 × 3 grid to partition the fluid space to be predicted. Using this theory, we developed and trained an artificial neural network (ANN) to predict the fluid dynamics within microfluidic mixers. This approach significantly reduces the time required for fluid evaluation. In this study, we designed a prototype microfluidic mixer and validated the reliability of our method by comparing it with predictions from traditional FEM software. The results show that our NGN model completes fluid predictions in just 40 s compared to approximately 10 min with FEM, with acceptable error margins. This technology achieves a 15-fold acceleration, greatly reducing the time and cost of microfluidic chip design.
