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Turbulent boundary layers are largely influenced by spatiotemporally developing coherent structures known as Large-Scale Motions (LSMs). This work examines the idea of creating synthetic hairpin trains, a model for LSMs, generated in a nominal zero pressure gradient laminar boundary layer. The study investigates the agreement between the experimentally measured flow field and the hairpin vortices and its simulated counterpart with a hybrid 2D inlet region. This approach uses time-varying unsteady spatially discrete velocity data obtained through experiments as an inflow boundary condition to the direct numerical simulation (DNS). A pre-processing divergence correction and interpolation scheme is employed to convert experimental data into a format better suited for the DNS. The matching is done by recreating a downstream flow using this hybrid physio-cyber approach. This method demonstrates the capability to produce a sequence of hairpins even with a simple 2D planar coarse dataset. A satisfactory qualitative and quantitative agreement was evident when comparing Q-criterion iso-surfaces of instantaneous DNS and phase-locked experimental data. The results of this study not only demonstrate the efficacy of the proposed approach in recreating LSMs but also suggest its applicability to future hybrid experimental-DNS flow control studies.