An Intelligent Microfluidics Enabled by Data‐Driven Deep Learning‐Based Microrobot‐Assisted Flow Manipulation

Microrobot‐driven microfluidic mixing offers active and reconfigurable flow control, yet its practical deployment is limited by complex time‐dependent flow interactions and the experimental burden associated with evaluating mixing outcomes. Here, a data‐driven deep learning framework is presented to enable predictive analysis and optimization of microrobot‐induced micromixing directly from experimental image data. A time‐conditioned conditional generative adversarial network (cGAN) is trained using experimentally acquired mixing images to learn the spatiotemporal evolution of dye distributions from an initial mixing state. The trained model accurately predicts future mixing images and corresponding mixing performance at distinct microrobot locations, achieving Pearson correlation coefficients approaching 0.98 between predicted and experimental results. Beyond scalar performance metrics, physically consistent spatial mixing patterns are reproduced by the cGAN, demonstrating that underlying microrobot–flow interactions are effectively learned. As a demonstration, a Bayesian optimization module is integrated with the trained cGAN surrogate model to identify optimal sequential microrobot actuation time allocations at different spatial locations, through which mixing performance exceeding 90% is achieved within a total actuation duration of 120 s without repeated physical experimentation. The presented work establishes a learning‐based foundation for smart microfluidics, in which deep generative models transform experimental images into predictive tools for microrobot‐enabled flow manipulation.