It is crucial to note, however, that nMSE can exceed 1 when model predictions are worse than this naive baseline, indicating very poor model performance. To address inherent uncertainties and improve the reliability of model evaluations, the analysis extends to aggregating nMSE over multiple time instants rather than relying on a singletime snapshots in order to have a more average in-time description. This method, involving the sum of nMSE values across multiple ovservation times 𝐾 as expressed in Eq. ( ), strengthens the robustness of the evaluation. This comprehensive assessment through the minimal total nMSE enables the inverse modelling technique to pinpoint the diffusion coefficient 𝐷 that most accurately corresponds to the experimental observations, offering a deeper insight into the dynamics of the system.