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.

翻译：热力学状态方程（EOS）对许多工业领域及学术界至关重要。即使不考虑数据采集所需的高昂且广泛的测量工作，EOS的开发也是一个极其耗时的过程，且目前仍严重依赖专家知识和迭代微调。为改进并加速EOS开发流程，我们提出了热力学信息符号回归（TiSR），这是一种面向热力学EOS建模的符号回归（SR）工具。TiSR已具备强大的SR能力，并在https://doi.org/10.1007/s10765-023-03197-z 的研究中得到应用。其设计目标是将SR基础框架与扩展功能相结合，以处理常呈现强散度的实验数据、多种残差预处理与后处理选项，以及热力学EOS开发所需的其他特性。尽管TiSR尚未面向最终用户，本文旨在报告其当前状态、展示进展，并讨论（近期及远期）未来发展方向。TiSR的代码库位于https://github.com/scoop-group/TiSR，引用方式参见https://doi.org/10.5281/zenodo.8317547。