Replication Code for: Greedy Kernel Methods for Approximating Breakthrough Curves for Reactive Flow from 3D Porous Geometry Data

In this paper we address the challenging application of 3D pore scale reactive flow under varying geometry parameters. We demonstrate that the vectorial kernel orthogonal greedy approximation (VKOGA) procedure with a data-adapted two-layer kernel yields excellent predictors for learning from 3D geometry voxel data via both morphological descriptors or principal component analysis.

The numerical experiments from this paper can be reproduced using this code. See the README for more information and installation instructions.