How to cite this package

References

In case of using this pycc package, please cite [Gon26a]

Furthermore:

If using the NN-CC method, please cite [Gon24] and [GL25]
If using the Poly-CC method, please cite [Gon23] and [Gon24]
If using post-SR and/or SymbReg-CC methods, please cite [Gon26b] and [Cra23]

BibTeX

@article{Gonzalez2026pycc,
   title={PyCC.id: A package for hypothesis-driven equation discovery with structural identifiability},
   author={Federico J. Gonzalez},
   journal={arXiv preprint arXiv:2606.05191},
   year={2026},
   eprint={2606.05191},
   primaryClass={cs.LG},
   url={https://arxiv.org/abs/2606.05191},
}

@article{Gonzalez2026,
  title={Integrating prior knowledge in equation discovery: Interpretable symmetry-informed neural networks and symbolic regression via characteristic curves},
  author={Gonzalez, Federico J.},
  journal={arXiv preprint arXiv:2601.21720},
  year={2026},
  eprint={2601.21720},
  url={https://arxiv.org/abs/2601.21720},
}

@article{Gonzalez2025,
  title = {{Interpretable neural network system identification method for two families of second-order systems based on characteristic curves}},
  author = {Gonzalez,  Federico J. and Lara,  Luis P.},
  volume = {113},
  ISSN = {1573-269X},
  DOI = {10.1007/s11071-025-11744-6},
  number = {24},
  journal = {Nonlinear Dyn.},
  publisher = {Springer Science and Business Media LLC},
  year = {2025},
  month = sep,
  pages = {33063–33086}
}


@article{Gonzalez2023,
  title     = {Determination of the characteristic curves of a nonlinear first order system from Fourier analysis},
  author    = {Gonzalez, Federico J.},
  journal   = {Sci. Rep.},
  publisher = {Springer Science and Business Media LLC},
  volume    =  13,
  number    =  1,
  pages     = {1955},
  month     =  feb,
  year      =  2023,
  doi =    {10.1038/s41598-023-29151-5},
}

@article{Gonzalez2024,
  title = {System identification based on characteristic curves: a mathematical connection between power series and Fourier analysis for first-order nonlinear systems},
  author = {{F. J. Gonzalez}},
  volume = {112},
  issn = {1573-269X},
  url = {},
  doi = {10.1007/s11071-024-09890-4},
  number = {18},
  journal = {Nonlinear Dyn.},
  publisher = {Springer Science and Business Media LLC},
  year = {2024},
  month = jul,
  pages = {16167–16197}
}

@article{Cranmer2023,
  title={Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl},
  author={Miles Cranmer},
  journal={arXiv preprint arXiv:2305.01582},
  year={2023},
  eprint={2305.01582},
  url={https://arxiv.org/abs/2305.01582},
}

[Cra23]

Miles Cranmer. Interpretable machine learning for science with pysr and symbolicregression.jl. arXiv preprint arXiv:2305.01582, 2023. URL: https://arxiv.org/abs/2305.01582, arXiv:2305.01582.

[Gon24] (1,2)

Federico J Gonzalez. System identification based on characteristic curves: a mathematical connection between power series and Fourier analysis for first-order nonlinear systems. Nonlinear Dyn., 112(18):16167–16197, July 2024. URL:, doi:10.1007/s11071-024-09890-4.

[Gon23]

Federico J. Gonzalez. Determination of the characteristic curves of a nonlinear first order system from Fourier analysis. Sci. Rep., 13(1):1955, February 2023. doi:10.1038/s41598-023-29151-5.

[Gon26a]

Federico J. Gonzalez. Pycc.id: a package for hypothesis-driven equation discovery with structural identifiability. arXiv preprint arXiv:2606.05191, 2026. URL: https://arxiv.org/abs/2606.05191, arXiv:2606.05191.

[Gon26b]

Federico J. Gonzalez. Integrating prior knowledge in equation discovery: Interpretable symmetry-informed neural networks and symbolic regression via characteristic curves. arXiv preprint arXiv:2601.21720, 2026. URL: https://arxiv.org/abs/2601.21720, arXiv:2601.21720.

[GL25]

Federico J. Gonzalez and Luis P. Lara. Interpretable neural network system identification method for two families of second-order systems based on characteristic curves. Nonlinear Dyn., 113(24):33063–33086, September 2025. doi:10.1007/s11071-025-11744-6.