Holte, Serena2023-06-162023-06-162023-06https://hdl.handle.net/11299/254804A thesis [actually a Plan B] submitted to the faculty of the Graduate School of the University of Minnesota by Serena Holte in partial fulfillment of the requirements for the degree of Master of Science, June 2023.I have successfully generated a graphic user interface that predicts microfluidic droplet diameters from a neural network. The neural network inputs are fluid properties and geometries of 3D glass capillary devices. For water-in-oil single emulsions, the mean-squared error at the end of 100 epochs for training and validation converged to 7.2% and 7.4%, respectively. The deep machine learning model provides an alternative method of predicting droplet size without the need for rigorous theory. Moreover, the model can be altered to predict other microfluidic parameters or properties and could be extended to other fluids as well.enPlan Bs (project-based master's degrees)Master of ScienceMaster of Science in PhysicsDepartment of Physics and AstronomySwenson College of Science and EngineeringUniversity of Minnesota DuluthScholarly Text or Essay