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).
- hojun.you@sejong.ac.kr (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}
}