We present a novel approach to predicting the pressure and flow rate of flexible electrohydrodynamic pumps using the Kolmogorov-Arnold Network. Inspired by the Kolmogorov-Arnold representation theorem, KAN replaces fixed activation functions with learnable spline-based activation functions, enabling it to approximate complex nonlinear functions more effectively than traditional models like Multi-Layer Perceptron and Random Forest. We evaluated KAN on a dataset of flexible EHD pump parameters and compared its performance against RF, and MLP models. KAN achieved superior predictive accuracy, with Mean Squared Errors of 12.186 and 0.001 for pressure and flow rate predictions, respectively. The symbolic formulas extracted from KAN provided insights into the nonlinear relationships between input parameters and pump performance. These findings demonstrate that KAN offers exceptional accuracy and interpretability, making it a promising alternative for predictive modeling in electrohydrodynamic pumping.

翻译：我们提出了一种利用Kolmogorov-Arnold网络预测柔性电流体动力学泵压力和流量的新方法。受Kolmogorov-Arnold表示定理启发，KAN将固定激活函数替换为可学习的样条基激活函数，使其能够比多层感知机和随机森林等传统模型更有效地逼近复杂非线性函数。我们基于柔性EHD泵参数数据集对KAN进行评估，并将其性能与RF和MLP模型进行对比。KAN实现了优越的预测精度，压力和流量预测的均方误差分别为12.186和0.001。从KAN中提取的符号公式揭示了输入参数与泵性能之间的非线性关系。这些发现表明，KAN具有卓越的准确性和可解释性，使其成为电流体动力学泵预测建模领域极具前景的替代方案。