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...

Descripción completa

Detalles Bibliográficos
Autores principales: Bilbrey, Jenna A., Marrero, Carlos Ortiz, Sassi, Michel, Ritzmann, Andrew M., Henson, Neil J., Schram, Malachi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2020
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

Internet

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

Ejemplares similares