Generative AI for Microfluidic Device Design

Microfluidic devices enable precise manipulation of fluids at the microscale and are widely used in biomedical diagnostics, drug delivery, and lab-on-a-chip systems. Traditional microfluidic device design relies heavily on iterative simulations and expert-driven trial-and-error processes, which are time-consuming and computationally expensive. Recent advances in generative artificial intelligence offer new opportunities to automate and optimize microfluidic design by learning complex flow patterns and geometries from data. This paper explores the application of generative AI models for microfluidic device design, including variational autoencoders, generative adversarial networks, and diffusion-based models. The study reviews design methodologies, simulation integration, performance evaluation, and emerging research directions. Results indicate that generative AI can significantly reduce design time while improving flow efficiency, mixing performance, and device robustness.