The field of machine learning is witnessing a significant shift towards incorporating concepts from thermodynamics to develop innovative and efficient algorithms. Researchers are exploring the potential of thermodynamic principles to advance machine learning models, enabling them to better handle complex and heterogeneous data. This direction is leading to the creation of novel, energy-based decision-making systems that offer interpretable and probabilistic interpretations. Notably, the use of thermodynamic-inspired approaches is resulting in models that are computationally efficient and easy to integrate into existing workflows. Some particularly noteworthy papers in this area include:

• Boltzmann Classifier, which proposes a novel classification algorithm inspired by the thermodynamic principles underlying the Boltzmann distribution.
• Vendi Information Gain, which introduces a novel alternative to mutual information that leverages the Vendi scores to account for similarity and generalizes mutual information under certain assumptions.
• ZENN, which proposes a thermodynamics-inspired computational framework for heterogeneous data-driven modeling that extends zentropy theory into the data science domain.