Fig. 4. Example of an application of machine-guided microfluidic control in its implementation to optimize droplet generation at multiple length scales.⁹⁵ (A) After an initial sampling of the parameter space, (B) a small-scale dataset is generated and (C) automatically analyzed using computer vision methods. (D) These results are then fed into a Bayesian decision policy that determines the next set of data to generate. (E) This iterative loop continues until performance is optimized and the boundaries of the stable droplet generation regime is identified. Reprinted with permission from Siemenn et al., 2022.⁹⁵ Copyright 2022 American Chemical Society.