This paper introduces a Nonlinear Auto-Regressive eXogenous Artificial Neural Network (NARX-ANN) model for prediction of spreading dynamics. 1220 experimental data of spreading dynamics of different droplets on various substrates have been collected from literature for model development. The model input parameters are Weber number, Ohnesorge number, and the tangent of equilibrium contact angle. An auxiliary input has been also added to take into account the irregular time sampling. D-optimal design of experiments has been utilized to determine the best parameters for the NARX model. It was found that the prediction capability of NARX model was better in comparison with KC and AGM models. The statistical parameters of the best developed NARX model including mean square error $\left(\text{M}\text{S}\text{E}\right)=3.257\times {10}^{-4}$, average absolute relative deviation (AARD) = 4.240%, and coefficient of determination $(R^2)=0.993$ demonstrate the excellent prediction capability of the proposed model.

本文介绍了一种非线性自回归异质人工神经网络（NARX-ANN）模型，用于预测扩散动力学。从文献中收集了1220种不同液滴在各种基质上扩散动力学的实验数据，用于模型开发。模型输入参数为韦伯数，欧姆尼佐数和平衡接触角的正切值。还添加了辅助输入，以考虑到不规则的时间采样。D优化实验设计已被用来确定NARX模型的最佳参数。结果表明，NARX模型的预测能力优于KC和AGM模型。最佳开发的NARX模型的统计参数，包括均方误差$\left(\text{中号}\text{小号}\text{Ë}\right)=3.257\times {10}^{--4}$，平均绝对相对偏差（AARD）= 4.240％和确定系数 $（{\mathrm{[R}}^2）=0.993$ 证明了所提出模型的出色预测能力。