Introducing Thermodynamics-Informed Symbolic Regression -- A Tool for Thermodynamic Equations of State Development

TL;DR

This paper introduces Thermodynamics-Informed Symbolic Regression (TiSR), a novel tool designed to accelerate and improve the development of thermodynamic equations of state by integrating thermodynamic principles into symbolic regression techniques.

Contribution

The paper presents TiSR, a symbolic regression tool tailored for thermodynamic EOS modeling that handles scattered data and incorporates thermodynamic constraints, advancing EOS development methods.

Findings

TiSR effectively models thermodynamic EOS from experimental data.

TiSR incorporates thermodynamic principles into symbolic regression.

The tool shows promising results in accelerating EOS development.

Abstract

Thermodynamic equations of state (EOS) are essential for many industries as well as in academia. Even leaving aside the expensive and extensive measurement campaigns required for the data acquisition, the development of EOS is an intensely time-consuming process, which does often still heavily rely on expert knowledge and iterative fine-tuning. To improve upon and accelerate the EOS development process, we introduce thermodynamics-informed symbolic regression (TiSR), a symbolic regression (SR) tool aimed at thermodynamic EOS modeling. TiSR is already a capable SR tool, which was used in the research of https://doi.org/10.1007/s10765-023-03197-z. It aims to combine an SR base with the extensions required to work with often strongly scattered experimental data, different residual pre- and post-processing options, and additional features required to consider thermodynamic EOS development. Although TiSR is not ready for end users yet, this paper is intended to report on its current state, showcase the progress, and discuss (distant and not so distant) future directions. TiSR is available at https://github.com/scoop-group/TiSR and can be cited as https://doi.org/10.5281/zenodo.8317547.