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Pore scale modeling is expensive, in terms of time, and computing resources. Some applications require to run many CFD simulations in similar geometries which is very time consuming, especially since the information from previous simulations can't be used in new runs. On the other hand, machine learning models are an alternative to learn from existing simulation data to be able to perform fast inference in new geometries. Nevertheless, it is a well-known fact that these models do not work out of the box, and require expert knowledge of the specific application.