Solitons, stable nonlinear waves, are critical for modeling biological wave phenomena, such as blood flow in arteries. Blood vessels’ nonlinear properties and elasticity make them ideal for soliton propagation. Using the reductive perturbation method, blood flow equations can be simplified to the Korteweg-de Vries (KdV) equation. This study employs Physics-Informed Neural Networks (PINNs) to model soliton propagation in arteries, integrating the KdV equation into a neural network with two hidden layers and a Tanh activation function, offering an efficient alternative to classical numerical methods.
