Measurement of the velocity field in thermal-hydraulic experiments is of great importance for phenomena interpretation and code validation. Direct measurement by means of Particle Image Velocimetry (PIV) is challenging in some multiphase's tests where the measurement system would be strongly affected by the phase interaction. A typical example can refer to the test with steam injection into a water pool where the rapid collapse of bubbles and significant temperature gradient makes it impossible to obtain main flow information in a relatively large steam flux. The goal of this work is to investigate the capability of the use of machine learning for the flow reconstruction of the jet induced by steam condensation from sparse temperature measurement with ThermoCouples (TCs). Two frameworks of (i) 'FDD' using pure data-driven modeling and (ii) 'FPINN' combining data-driven and Physics-Informed Neural Networks (PINN) are proposed and investigated. The frameworks are applied to a single-phase turbulent planar jet with data generated by CFD simulations.