Representation of the PINN architecture used in this study. The neural network 𝒩 typically consists of three hidden layers with 250 neurons each. Automatic differentiation is used to compute derivatives of the predicted fields, which are then used to evaluate the residuals of the governing equations. Two additional trainable variables are initialized alongside the neural network’s parameters in order to identify the values of μ_s and μ₂.