  Microfluidics have shown great promise in multiple applications, especially
in biomedical diagnostics and separations. While the flow properties of these
microfluidic devices can be solved by numerical methods such as computational
fluid dynamics (CFD), the process of mesh generation and setting up a numerical
solver requires some domain familiarity, while more intuitive commercial
programs such as Fluent and StarCCM can be expensive. Hence, in this work, we
demonstrated the use of a U-Net convolutional neural network as a surrogate
model for predicting the velocity and pressure fields that would result for a
particular set of microfluidic filter designs. The surrogate model is fast,
easy to set-up and can be used to predict and assess the flow velocity and
pressure fields across the domain for new designs of interest via the input of
a geometry-encoding matrix. In addition, we demonstrate that the same
methodology can also be used to train a network to predict pressure based on
velocity data, and propose that this can be an alternative to numerical
algorithms for calculating pressure based on velocity measurements from
particle-image velocimetry measurements. Critically, in both applications, we
demonstrate prediction test errors of less than 1%, suggesting that this is
indeed a viable method.
