  We show that artificial neural networks (ANNs) can, to high accuracy,
determine the topological invariant of a disordered system given its
two-dimensional real-space Hamiltonian. Furthermore, we describe a
"renormalization-group" (RG) network, an ANN which converts a Hamiltonian on a
large lattice to another on a small lattice while preserving the invariant. By
iteratively applying the RG network to a "base" network that computes the Chern
number of a small lattice of set size, we are able to process larger lattices
without re-training the system. We therefore show that it is possible to
compute real-space topological invariants for systems larger than those on
which the network was trained. This opens the door for computation times
significantly faster and more scalable than previous methods.
