Detecting subtle macroscopic changes in a finite temperature classical
  scalar field with machine learning
Jiming Yang Yutong Zheng Jiahong Zhou Huiyu Li Jun Yin
The ability to detect macroscopic changes is important for probing the
behaviors of experimental many-body systems from the classical to the quantum
realm. Although abrupt changes near phase boundaries can easily be detected,
subtle macroscopic changes are much more difficult to detect as the changes can
be obscured by noise. In this study, as a toy model for detecting subtle
macroscopic changes in many-body systems, we try to differentiate scalar field
samples at varying temperatures. We compare different methods for making such
differentiations, from physics method, statistics method, to AI method. Our
finding suggests that the AI method outperforms both the statistical method and
the physics method in its sensitivity. Our result provides a proof-of-concept
that AI can potentially detect macroscopic changes in many-body systems that
elude physical measures.