Machine learning outperforms thermodynamics in measuring how well a many-body system learns a drive

Area of Science:

• Statistical Mechanics
• Machine Learning
• Complex Systems

Background:

• Many-body systems, including suspensions and polymers, exhibit learning and memory of external patterns.
• Previous detection of this learning relied on macroscopic thermodynamic properties, limiting scope and precision.
• Existing methods are insufficient for comprehensively understanding the nuances of many-body learning.

Purpose of the Study:

• To develop a novel framework for quantifying statistical mechanical learning in many-body systems.
• To move beyond traditional thermodynamic measures by employing machine learning techniques.
• To provide a more precise and unifying method for detecting and measuring self-organization and learning in matter.

Main Methods:

• Utilized representation learning, a machine learning approach where information is processed through a bottleneck.
• Calculated bottleneck properties to quantify four key facets of learning: classification, memory capacity, discrimination, and novelty detection.
• Applied and illustrated the technique using numerical simulations of a classical spin glass.

Main Results:

• Successfully quantified multiple facets of many-body learning, including classification ability, memory capacity, discrimination ability, and novelty detection.
• Demonstrated that the developed toolkit can reveal self-organization phenomena not detectable by thermodynamic measures.
• Showcased enhanced reliability and precision in detecting and quantifying learning in matter compared to existing methods.

Conclusions:

• The proposed representation learning toolkit offers a powerful new approach to understanding and quantifying learning in diverse many-body systems.
• This method provides a unifying framework for studying many-body learning, applicable beyond equilibrium contexts.
• The findings open avenues for leveraging many-body learning in computation, memory, and engineering applications.