Infinitely differentiable Data-driven model for high temperature Equilibrium Air

IDEA is an open-source library supporting infinitely differentiable ANN-based models designed to predict thermodynamic and transport properties of 11-species (N2, O2, N, O, NO, NO+, N+, O+, N++, O++, e-) equilibrium air at high temperature (up to 25000 K).

• [email protected] (Hojun You)

A Python wrapper (pyIDEA) for IDEA is provided by Prof. Jinseok Park (Inha University) at this link: https://gitlab.com/jspark_aadl/pyidea.

Please cite the following article when mentioning IDEA in your own papers.

• Hojun You, Juhyun Kim, Kyeol Yune, and Chongam Kim, IDEA: Artificial Neural Network Models for 11-species Air Properties at Thermochemical Equilibrium. Computer Physics Communications, Vol. 290, 2023, 108788. Link

Bibtex

@article{You2022artificial,
  title   = {{IDEA: Artificial Neural Network Models for 11-species Air Properties at Thermochemical Equilibrium}},
  author = {You, Hojun and Kim, Juhyun and Yune, Kyeol and Kim, Chongam},
  journal = {Computer Physiccs Communications},
  volume={290},
  year = {2023},
  pages = {108788},
  doi = {10.1016/j.cpc.2023.108788}
}