Reaction diffusion system prediction based on convolutional neural network
Reaction diffusion system prediction based on convolutional neural network
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DOI：
10.1038/s41598-020-60853-2
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发表时间：
2020-03-03
影响因子：
4.6
通讯作者：
Zhang, Yongjie Jessica
中科院分区：
文献类型：
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作者：
Li, Angran;Chen, Ruijia;Zhang, Yongjie Jessica
反应扩散系统在化学中自然地被用来表示物质在空间域上的反应和扩散。它的解决方案说明了化学反应的基本过程，并显示了物质的不同空间模式。有限元等数值方法被广泛应用于求解反应扩散方程组的近似解。然而，当系统变得复杂时，这些方法需要很长的计算时间和巨大的计算资源。本文利用机器学习方法研究了一类二维单组份反应扩散系统的物理问题。设计和训练了一种基于编解码器的卷积神经网络(CNN)，以绕过昂贵的有限元计算过程，直接预测浓度分布。该学习模型考虑了不同的仿真参数、边界条件、几何构型和时间作为输入特征。特别是，经过训练的CNN模型通过输入时间特征来学习反应扩散系统的时间相关行为。因此，该模型能够以较高的测试精度(平均相对误差)直接提供特定时间的浓度预测
The reaction-diffusion system is naturally used in chemistry to represent substances reacting and diffusing over the spatial domain. Its solution illustrates the underlying process of a chemical reaction and displays diverse spatial patterns of the substances. Numerical methods like finite element method (FEM) are widely used to derive the approximate solution for the reaction-diffusion system. However, these methods require long computation time and huge computation resources when the system becomes complex. In this paper, we study the physics of a two-dimensional one-component reaction-diffusion system by using machine learning. An encoder-decoder based convolutional neural network (CNN) is designed and trained to directly predict the concentration distribution, bypassing the expensive FEM calculation process. Different simulation parameters, boundary conditions, geometry configurations and time are considered as the input features of the proposed learning model. In particular, the trained CNN model manages to learn the time-dependent behaviour of the reaction-diffusion system through the input time feature. Thus, the model is capable of providing concentration prediction at certain time directly with high test accuracy (mean relative error