Bilbrey Jenna A, Marrero Carlos Ortiz, Sassi Michel, Ritzmann Andrew M, Henson Neil J, Schram Malachi
Pacific Northwest National Laboratory, 902 Battelle Boulevard, P.O. Box 999, Richland, Washington 99352, United States.
ACS Omega. 2020 Feb 28;5(9):4588-4594. doi: 10.1021/acsomega.9b04104. eCollection 2020 Mar 10.
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.
我们应用递归神经网络（RNN）来预测由一组相互关联的化学反应产生的多种物质浓度分布随时间的演变。作为我们方法概念的证明，RNN在一个合成数据集上进行训练，该数据集通过求解核反应堆事故后可能发生的水相无机碘反应系统的动力学方程生成。我们研究了获得准确预测所需的最小数据集，并探索了RNN在面对以前未见过的数据时进行内插和外推的能力。我们还通过评估数据集上训练初始化的稳健性来研究我们的RNN的局限性。
