Recent advances in imaging techniques and related modeling have made it
possible to study micron and sub-micron scales in unprecedented detail.
Currently, performing direct simulations at a representative scale has
proven computationally prohibitive, hence, the ability to simulate flow
cannot keep up with the size of images available (e.g. x-ray
micro-tomography or large area scanning electron microscopy). To
overcome this issue, we propose to train a neural network architecture
to understand relationships between pore-scale morphology and the
simulation outputs. With this, we improve our portability and prediction
time. Convolutional neural networks are attractive for this task because
they support flexible input size, they are able to capture local
interactions, and they can find places that present similar patterns.
Generally, deeper networks have better prediction performance, but they
are very difficult to train due to the vanishing gradient problem. Also,
information from different scales is commonly lost along the network.