Lab-on-a-chip systems can be functionally decomposed into their basic operating devices. Common devices are mixers, reactors, injectors, and separators. In this work, the injector device is modeled using artificial neural networks trained with finite element simulations of the underlying mass transport PDE’s. This technique is used to map the injector behavior into a set of analytical performance functions parameterized by the system’s physical variables. The injector examples shown are the cross, double-tee, and gatedcross. The results are four orders of magnitude faster than numerical simulation and accurate with mean square errors on the order of 10
. The resulting neural network training data compares favorably with experimental data from a gated-cross injector found in the literature.