Thermodynamics of Learning Physical Phenomena

Thermodynamics could be seen as an expression of physics at a high epistemic level. As such, its potential as an inductive bias to help machine learning procedures attain accurate and credible predictions has been recently realized in many fields. We review how thermodynamics provides helpful insights in the learning process. At the same time, we study the influence of aspects such as the scale at which a given phenomenon is to be described, the choice of relevant variables for this description or the different techniques available for the learning process.

Automated summary

Thermodynamics, as a higher level of physics, has the potential to aid accurate and credible predictions in machine learning. This review explores how thermodynamics provides insights in the learning process, considering factors such as scale, choice of variables, and learning techniques.