In many problems represented by a mathematical model, the knowledge of the solution at different values of specific parameters is necessary and one way to proceed is to compute a high-fidelity simulation; however, since a high-fidelity simulation is very computational demanding, this is often too time consuming. In these situations, surrogate models can be very useful. The aim of this work is to show viability of Machine Learning as a surrogate model in a specific setting. The objective is to obtain a reconstruction of a high-fidelity solution of a fluid dynamics problem, depending on both a specific Reynolds number and a specific time, without executing the relative simulation, with the aim of accelerating the case where thousands of simulations are required.