To this aim, the predictions are compared with experimental observations to identify the optimal solution that minimizes the nMSE or the MSE between the experimental data and model predictions. After that, the computational efficiency of the framework is scrutinized. Figure offers a schematic overview of the key components and stages of the framework. Notably, the steps enclosed within the light purple dashed lines are key numerical procedures of the framework and will be thoroughly explained in this section. It is also important to note that the blocks of the methods employed in various stages of the proposed framework are not unique and can be interchanged with alternative methods adapting to the problem of interest. For instance, the LBM physics solver could be replaced by a finite difference or finite volume methods, or by either scientific or commercial software. Similarly, the production of the surrogate model which in the presented case is done using ANNs, could be conducted by a large number of other ML techniques, e.g. gaussian processes , tree-based methods , an interpolation scheme , or other statistical techniques . In this study ANNs are preferred because of the potential they offer to generalize the entire modelling framework to higher dimensions .