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Turbulence is a complex phenomenon that has a chaotic nature with multiplespatio-temporal scales, making predictions of turbulent flows a challengingtopic. Nowadays, an abundance of high-fidelity databases can be generated byexperimental measurements and numerical simulations, but obtaining suchaccurate data in full-scale applications is currently not possible. Thismotivates utilising deep learning on subsets of the available data to reducethe required cost of reconstructing the full flow in such full-scaleapplications. Here, we develop a generative-adversarial-network (GAN)-basedmodel to reconstruct the three-dimensional velocity fields from flow datarepresented by a cross-plane of unpaired two-dimensional velocity observations.The model could successfully reconstruct the flow fields with accurate flowstructures, statistics and spectra. The results indicate that our model can besuccessfully utilised for reconstructing three-dimensional flows fromtwo-dimensional experimental measurements. Consequently, a remarkable reductionof the experimental setup cost can be achieved.

A deep-learning approach for reconstructing 3D turbulent flows from 2D observation data