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.

翻译：热力学可以被看作是物理学在高认知水平上的表达,因此,它作为帮助机器学习程序实现准确和可信的预测的感性偏向,最近在许多领域已经实现。我们审查热力学如何在学习过程中提供有益的见解。与此同时,我们研究诸如描述特定现象的规模、为描述该现象选择相关变量或学习过程可用的不同技术等方面的影响。