Tradition of Data-Intensive Use: The Example of Domain Thermophysics. Methods and Algorithms

Using the example of thermophysics, the evolution of approaches to working with scientific data on substances and materials properties is traced. This paper shows that across all stages, thermophysics can be classified as a data-intensive science, characterized by a focus on working with data, including its storage, its organization, and the extraction of meaningful information. It presents improvements in processing methods that are associated with the use of new information technologies, including machine learning techniques. Their potential for the field of thermodynamics relative to traditional statistical methods is analyzed. In this context, the general issue of the relationship between statistics and data science which has generated extensive debate in literature and online is discussed. All the authors’ conclusions are based on an analysis of specific issues related to the prediction of the properties of substances and constructing the equation of state and thermodynamic models for multicomponent systems.