One may question why we do not employ t-SNE 41 directly to reduce the dimensionality of the 3D porosity images. The inventors of t-SNE themselves first used principal component analysis (directly related to SVD) to reduce the dimensionality of images of handwritten digits preceding t-SNE to filter out noise and to speed up the computation. Our reasons for employing SVD instead of t-SNE directly are (i) the mapping by SVD lends more interpretability through visualizing the eigencages (Fig. ), (ii) unlike t-SNE, SVD provides an explicit mapping function that can be used to (a) project unseen data into the latent space as we did with our MD snapshots and (b) generate fictitious pore structures as we did between NC2 and DC1 (iii) t-SNE is stochastic, whereas SVD is deterministic.