Dataset Information

Tracking the Chemical Evolution of Iodine Species Using Recurrent Neural Networks.

ABSTRACT: 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 the kinetic equations of a system of aqueous inorganic iodine reactions that can follow after nuclear reactor accidents. We examine the minimum dataset necessary to obtain accurate predictions and explore the ability of RNNs to interpolate and extrapolate when exposed to previously unseen data. We also investigate the limits of our RNN by evaluating the robustness of the training initialization on our dataset.

SUBMITTER: Bilbrey JA

PROVIDER: S-EPMC7066558 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Publications

Tracking the Chemical Evolution of Iodine Species Using Recurrent Neural Networks.

Bilbrey Jenna A JA Marrero Carlos Ortiz CO Sassi Michel M Ritzmann Andrew M AM Henson Neil J NJ Schram Malachi M

ACS omega 20200228 9

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 the kinetic equations of a system of aqueous inorganic iodine reactions that can follow after nuclear reactor accidents. We examine the minimum dataset necessary to obtain accurate predictions and explore the ability of ...[more]

PMID: 32175505

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Dataset Information

Tracking the Chemical Evolution of Iodine Species Using Recurrent Neural Networks.

Publications

Tracking the Chemical Evolution of Iodine Species Using Recurrent Neural Networks.

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