Watch and learn -- a generalized approach for transferrable learning in deep neural networks via physical principles

Abstract

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.

References in corpus (9)

• Normalizing Flows: An Introduction and Review of Current Methods
• Inverse statistical problems: from the inverse Ising problem to data science
• Recurrent Neural Network Wave Functions
• Machine Learning Phases of Strongly Correlated Fermions
• Solving Statistical Mechanics Using Variational Autoregressive Networks
• Asymptotically unbiased estimation of physical observables with neural samplers
• Extensive deep neural networks for transferring small scale learning to large scale systems
• Deep neural networks for direct, featureless learning through observation: the case of 2d spin models
• Emergent Quantum Mechanics in an Introspective Machine Learning Architecture

Cited by in corpus (3)

• Machine Learning for Quantum Matter
• Gauge Invariant and Anyonic Symmetric Transformer and RNN Quantum States for Quantum Lattice Models
• Physics-informed graph neural networks enhance scalability of variational nonequilibrium optimal control