==========
How to cite this package
==========
 
References
-----------

In case of using this pycc package, please cite :cite:`Gonzalez2026pycc`

Furthermore: 
  * If using the **NN-CC** method, please cite :cite:`Gonzalez2024` and :cite:`Gonzalez2025` 

  * If using the **Poly-CC** method, please cite :cite:`Gonzalez2023` and :cite:`Gonzalez2024`
  
  * If using **post-SR** and/or **SymbReg-CC** methods, please cite :cite:`Gonzalez2026` and :cite:`Cranmer2023`
  

..
     [1] Gonzalez, F. J. Arxiv joss arxiv (2025)
..
     [2] Gonzalez, F. J. "`Integrating prior knowledge in equation discovery: Interpretable symmetry-informed neural networks and symbolic regression via characteristic curves <https://doi.org/10.48550/arXiv.2601.21720>`_." arXiv preprint arXiv:2601.21720 (2026)
..
     [3] Gonzalez, F. J. and Lara, L. P. "`Interpretable neural network system identification method for two families of second-order systems based on characteristic curves <https://doi.org/10.1007/s11071-025-11744-6>`_." Nonlinear Dyn. (2025)
..
     [4] Gonzalez, F.J. "`Determination of the characteristic curves of a nonlinear first order system from fourier analysis <https://doi.org/10.1038/s41598-023-29151-5>`_." Sci. Rep., vol. 13, 1955, (2023).
..
      [5] Gonzalez, F.J. "`System identification based on characteristic curves: a mathematical connection between power series and Fourier analysis for first-order nonlinear systems <https://doi.org/10.1007/s11071-024-09890-4>`_." Nonlinear Dyn. 112, 16167–16197 (2024).
..
      [6] Cranmer, M. "`Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl <https://doi.org/10.48550/arXiv.2305.01582>`_." arXiv preprint arXiv:2305.01582 (2023).




BibTeX
------

.. code-block:: 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}, 
   }
   
   
   
.. bibliography::
   :all: