  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.
