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Tracking the Chemical Evolution of Iodine Species Using Recurrent Neural Networks
[Image: see text] We apply recurrent neural networks (RNNs) to predict the time evolution of the concentration profile of multiple species resulting from a set of interconnected chemical reactions. As a proof of concept of our approach, RNNs were trained on a synthetic dataset generated by solving t...
| Autores principales: | , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
American Chemical Society
2020
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7066558/ https://www.ncbi.nlm.nih.gov/pubmed/32175505 http://dx.doi.org/10.1021/acsomega.9b04104 |