Singapore University of Technology and Design, 8 Somapah Road, Singapore, 487372, Singapore.
Sci Rep. 2019 Aug 2;9(1):11242. doi: 10.1038/s41598-019-47747-8.
The lateral-line system that has evolved in many aquatic animals enables them to navigate murky fluid environments, locate and discriminate obstacles. Here, we present a data-driven model that uses artificial neural networks to process flow data originating from a stationary sensor array located away from an obstacle placed in a potential flow. The ability of neural networks to estimate complex underlying relationships between parameters, in the absence of any explicit mathematical description, is first assessed with two basic potential flow problems: single source/sink identification and doublet detection. Subsequently, we address the inverse problem of identifying an obstacle shape from distant measures of the pressure or velocity field. Using the analytical solution to the forward problem, very large training data sets are generated, allowing us to obtain the synaptic weights by means of a gradient-descent based optimization. The resulting neural network exhibits remarkable effectiveness in predicting unknown obstacle shapes, especially at relatively large distances for which classical linear regression models are completely ineffectual. These results have far-reaching implications for the design and development of artificial passive hydrodynamic sensing technology.
许多水生动物进化出的侧线系统使它们能够在浑浊的流体环境中导航，定位和区分障碍物。在这里，我们提出了一个数据驱动的模型，该模型使用人工神经网络处理源自放置在势流中障碍物的静止传感器阵列的流动数据。神经网络在没有任何显式数学描述的情况下估计参数之间复杂的潜在关系的能力，首先通过两个基本的势流问题进行评估：单源/汇识别和偶极子检测。随后，我们解决了从远处测量压力或速度场来识别障碍物形状的逆问题。使用正向问题的解析解，可以生成非常大的训练数据集，从而使我们可以通过基于梯度下降的优化来获得突触权重。所得神经网络在预测未知障碍物形状方面表现出非凡的有效性，尤其是在距离较远的情况下，经典的线性回归模型完全无效。这些结果对人工被动水动力传感技术的设计和开发具有深远的意义。
