  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.
