A Relational Macrostate Theory Guides Artificial Intelligence to Learn Macro and Design Micro

TL;DR

This paper introduces a relational macrostate theory and a machine learning architecture, MacroNet, to identify and design macrostates in complex systems by leveraging symmetry relations between observations.

Contribution

It presents a novel macrostate theory based on symmetries and develops MacroNet to identify and design macrostates across simple to complex systems.

Findings

Successfully identified macrostates in harmonic oscillators and Turing patterns.

Enabled inverse design of microstates with desired macroscopic properties.

Unified approach to macrostate identification and design across system complexities.

Abstract

The high-dimesionality, non-linearity and emergent properties of complex systems pose a challenge to identifying general laws in the same manner that has been so successful in simpler physical systems. In Anderson's seminal work on why "more is different" he pointed to how emergent, macroscale patterns break symmetries of the underlying microscale laws. Yet, less recognized is that these large-scale, emergent patterns must also retain some symmetries of the microscale rules. Here we introduce a new, relational macrostate theory (RMT) that defines macrostates in terms of symmetries between two mutually predictive observations, and develop a machine learning architecture, MacroNet, that identifies macrostates. Using this framework, we show how macrostates can be identifed across systems ranging in complexity from the simplicity of the simple harmonic oscillator to the much more complex spatial patterning characteristic of Turing instabilities. Furthermore, we show how our framework can be used for the inverse design of microstates consistent with a given macroscopic property -- in Turing patterns this allows us to design underlying rule with a given specification of spatial patterning, and to identify which rule parameters most control these patterns. By demonstrating a general theory for how macroscopic properties emerge from conservation of symmetries in the mapping between observations, we provide a machine learning framework that allows a unified approach to identifying macrostates in systems from the simple to complex, and allows the design of new examples consistent with a given macroscopic property.