In this section, the physics-based ML surrogate model is designed to efficiently approximate the forward prediction of concentration (𝐶) at any specific times (𝑡), central axis coordinates (𝑥), and diffusion coefficient (𝐷) (Figure ). This model, once trained on datasets from a 3D physical simulator, can provide rapid predictions at almost zero computational cost. The extremely high computational efficiency of the ANN surrogate model, after its initial training, is necessary for the inverse modelling objective through different optimization algorithms, which in this context involves fitting the 𝐷 to the experimentally obtained Br⁻ concentration profiles.