  Transfer learning refers to the use of knowledge gained while solving a
machine learning task and applying it to the solution of a closely related
problem. Such an approach has enabled scientific breakthroughs in computer
vision and natural language processing where the weights learned in
state-of-the-art models can be used to initialize models for other tasks which
dramatically improve their performance and save computational time. Here we
demonstrate an unsupervised learning approach augmented with basic physical
principles that achieves fully transferrable learning for problems in
statistical physics across different physical regimes. By coupling a sequence
model based on a recurrent neural network to an extensive deep neural network,
we are able to learn the equilibrium probability distributions and
inter-particle interaction models of classical statistical mechanical systems.
Our approach, distribution-consistent learning, DCL, is a general strategy that
works for a variety of canonical statistical mechanical models (Ising and
Potts) as well as disordered (spin-glass) interaction potentials. Using data
collected from a single set of observation conditions, DCL successfully
extrapolates across all temperatures, thermodynamic phases, and can be applied
to different length-scales. This constitutes a fully transferrable
physics-based learning in a generalizable approach.
