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A functional modeling technique is developed for
components of a microfluidic system and applied to three
common injector topologies. This technique uses sparse
numerical simulation to train a neural network to provide
compact, explicit, and accurate component models. The
resulting models are compatible with analytic system
simulation environments, making complex design synthesis
and optimization feasible, unlike standard techniques using
computationally expensive numerical simulation. The
neural network models are accurate to numerical simulation
with mean squared errors less than 10-4. In explicit form,
the neural network models are very fast taking less than 1s
per evaluation.
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