When Machine Learning Meets Multiscale Modeling in Chemical Reactions

Wuyue Yang Zhou Pei-Yuan Center for Applied Mathematics, Tsinghua, Beijing, 100084, P.R. China Liangrong Peng College of Mathematics and Data Science, Minjiang University, Fuzhou, 350108, P.R. China Yi Zhu Zhou Pei-Yuan Center for Applied Mathematics, Tsinghua, Beijing, 100084, P.R. China Liu Hong Zhou Pei-Yuan Center for Applied Mathematics, Tsinghua, Beijing, 100084, P.R. China

TBD mathscidoc:2204.43011

2020.6
Due to the intrinsic complexity and nonlinearity of chemical reactions, direct applications of traditional machine learning algorithms may face with many difficulties. In this study, through two concrete examples with biological background, we illustrate how the key ideas of multiscale modeling can help to reduce the computational cost of machine learning a lot, as well as how machine learning algorithms perform model reduction automatically in a time-scale separated system. Our study highlights the necessity and effectiveness of an integration of machine learning algorithms and multiscale modeling during the study of chemical reactions.
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@inproceedings{wuyue2020when,
  title={When Machine Learning Meets Multiscale Modeling in Chemical Reactions},
  author={Wuyue Yang, Liangrong Peng, Yi Zhu, and Liu Hong},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20220422135349363877092},
  year={2020},
}
Wuyue Yang, Liangrong Peng, Yi Zhu, and Liu Hong. When Machine Learning Meets Multiscale Modeling in Chemical Reactions. 2020. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20220422135349363877092.
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