Numerical simulation of mass transfer in biphase flows is a fundamental tool in various disciplines. One major issue is related to the thermodynamics of the liquid-vapor mixture. Usually, convex equations of state are used, where a real sound speed can be defined under the saturation curve, such as for exemple the Stiffened Gas (SG) equation. Neverthless, the use of this equation in the gas phase, ban the prediction of real-gas effects, demanding a more complex equation of state, generally non-convex. The aim of this work is to formulate an innovative algorithm for a strong coupling between a SG equation and a whatever more complex equation for the gas phase, using experimental data. The proposed algorithm relies on a bayesian-based method, taking into account model and data uncertainties
