Accelerating a Morphology-Preserving Adsorption Model by Deep Learning

Abstract

Physico-chemical simulations are often based on time consuming statistical microstructures models. Recent works show that mathematical morphology and non-linear image processing operators could provide less time-consuming alternatives [1]. Even though these models allow a significant improvement of computation cost, the solving time for a single run on a large microstructure may still require many hours. This paper proposes a two-step approach to overcome this issue in the field of gas adsorption modeling. The first step is to encompass physico-chemical calculation data into 3D adsorption maps. The second step is to train an encoder-decoder convolutional neural network on these maps, to estimate adsorption maps. Using this new approach, the computation time of the adsorption curve for a single run (512 voxels) was reduced by a factor of more than 30.