... campaigns. Design/methodology/approach The methodology is based on a surrogate data-driven physics-aware model. Simplified CFD is used to generate a data-tensor-form surrogate model of the RDE. Then, the surrogate model feeds an algorithm that solves the direct problem (prediction) and the inverse...

... with resolution under 1 m in approximately 1 s. Findings The surrogate model accelerates predictions by a factor of 7200 compared to direct CFD simulations while maintaining acceptable accuracy: mean relative deviations in velocity predictions near the buildings of interest are of the order of 2%. Examples...

... algorithm with a multi-fidelity surrogate model. Machine learning based on artificial neural networks (ANN) is used as the modelling technique because of its ability to handle non-linear behaviour. The multi-fidelity method combines Reynolds-averaged Navier Stokes and Euler CFD calculations as high- and low...

... to create a generative surrogate model that can be applied on multi-objective optimization problems. Design/methodology/approach The latest backbone in the field of computer vision (Swin-Transformer, 2021) was introduced and improved as the surrogate function for prediction of the multi-physics field...