About the package
Arrhenius.jl is a Julia package for reacting-flow and combustion modeling, including differentiable chemistry workflows.
Publications
- W. Ji, X. Su, B. Pang, S. J. Cassady, A. M. Ferris, Y. Li, Z. Ren, R. Hanson, S. Deng, “Arrhenius.jl: A Differentiable Combustion Simulation Package”, arXiv:2107.06172 [physics.chem-ph], 2021.
- W. Ji, S. Deng, “Autonomous Discovery of Unknown Reaction Pathways from Data by Chemical Reaction Neural Network”, The Journal of Physical Chemistry A 125(4):1082–1092, 2021.
- W. Ji, J. Zanders, J.-W. Park, S. Deng, “Data-Driven Approaches to Learn HyChem Models”, ASME 2021 Internal Combustion Engine Division Fall Technical Conference (ICEF2021), 2021.
- X. Su, W. Ji, L. Zhang, W. Wu, Z. Ren, S. Deng, “Neural Differential Equations for Inverse Modeling in Model Combustors”, ASME 2021 International Mechanical Engineering Congress and Exposition (IMECE2021), Volume 11: Heat Transfer and Thermal Engineering, 2021.
- W. Ji, X. Su, B. Pang, Y. Li, Z. Ren, S. Deng, “SGD-based optimization in modeling combustion kinetics: Case studies in tuning mechanistic and hybrid kinetic models”, Fuel 324:124560, 2022.
- W. Ji, F. Richter, M. J. Gollner, S. Deng, “Autonomous kinetic modeling of biomass pyrolysis using chemical reaction neural networks”, Combustion and Flame 240:111992, 2022.
@misc{ji2021arrhenius,
title = {Arrhenius.jl: A Differentiable Combustion Simulation Package},
author = {Ji, Weiqi and Su, Xingyu and Pang, Bin and Cassady, Sean Joseph and Ferris, Alison M. and Li, Yujuan and Ren, Zhuyin and Hanson, Ronald and Deng, Sili},
year = {2021}, eprint = {2107.06172}, archivePrefix = {arXiv}, primaryClass = {physics.chem-ph},
url = {https://arxiv.org/abs/2107.06172}
}
@article{ji2021crnn,
title = {Autonomous Discovery of Unknown Reaction Pathways from Data by Chemical Reaction Neural Network},
author = {Ji, Weiqi and Deng, Sili},
journal = {The Journal of Physical Chemistry A}, volume = {125}, number = {4}, pages = {1082--1092}, year = {2021},
publisher = {American Chemical Society}, doi = {10.1021/acs.jpca.0c09316}
}
@inproceedings{ji2021hychem,
title = {Data-Driven Approaches to Learn HyChem Models},
author = {Ji, Weiqi and Zanders, Julian and Park, Ji-Woong and Deng, Sili},
booktitle = {ASME 2021 Internal Combustion Engine Division Fall Technical Conference}, series = {ICEF2021}, year = {2021},
publisher = {American Society of Mechanical Engineers}, doi = {10.1115/icef2021-67925}
}
@inproceedings{su2021neuralde,
title = {Neural Differential Equations for Inverse Modeling in Model Combustors},
author = {Su, Xingyu and Ji, Weiqi and Zhang, Long and Wu, Wantong and Ren, Zhuyin and Deng, Sili},
booktitle = {Volume 11: Heat Transfer and Thermal Engineering}, series = {IMECE2021}, year = {2021},
publisher = {American Society of Mechanical Engineers}, doi = {10.1115/imece2021-69657}
}
@article{ji2022sgd,
title = {SGD-based optimization in modeling combustion kinetics: Case studies in tuning mechanistic and hybrid kinetic models},
author = {Ji, Weiqi and Su, Xingyu and Pang, Bin and Li, Yujuan and Ren, Zhuyin and Deng, Sili},
journal = {Fuel}, volume = {324}, pages = {124560}, year = {2022}, publisher = {Elsevier}, doi = {10.1016/j.fuel.2022.124560}
}
@article{ji2022biomass,
title = {Autonomous kinetic modeling of biomass pyrolysis using chemical reaction neural networks},
author = {Ji, Weiqi and Richter, Franz and Gollner, Michael J. and Deng, Sili},
journal = {Combustion and Flame}, volume = {240}, pages = {111992}, year = {2022}, publisher = {Elsevier}, doi = {10.1016/j.combustflame.2022.111992}
}Related projects
- ArrheniusActiveSubspace: sensitivity analysis and uncertainty quantification.
- ArrheniusFlame1D: one-dimensional flame sensitivity analysis.
- CRNN_HyChem: neural-network approaches to learning kinetic mechanisms.
- DeepReduction: mechanism reduction workflows.
Related software includes Cantera for mechanism processing and reference calculations and ReactionMechanismSimulator.jl for chemical mechanism simulation.
For the package license, see the MIT License.