• Corpus ID: 245853731

A multi-scale sampling method for accurate and robust deep neural network to predict combustion chemical kinetics

@article{Zhang2022AMS,
  title={A multi-scale sampling method for accurate and robust deep neural network to predict combustion chemical kinetics},
  author={Tianhan Zhang and Yuxiao Yi and Yifan Xu and Zhi chao Chen and Yaoyu Zhang and E Weinan and Zhi-Qin John Xu},
  journal={ArXiv},
  year={2022},
  volume={abs/2201.03549}
}
Machine learning has long been considered as a black box for predicting combustion chemical kinetics due to the extremely large number of parameters and the lack of evaluation standards and reproducibility. The current work aims to understand two basic questions regarding the deep neural network (DNN) method: what data the DNN needs and how general the DNN method can be. Sampling and preprocessing determine the DNN training dataset, further affect DNN prediction ability. The current work… 

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Species reaction rate modelling based on physics-guided machine learning
A self-organizing-map approach to chemistry representation in combustion applications
TLDR
The relatively modest CPU-time and memory requirements of the method make the SOM-MLP approach a promising technique for the inclusion of large chemical mechanisms in the context of complex applications, such as the multidimensional simulation of combustion.
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