The mathematical modelling of a system is an activity as complex as it is useful for many fields of work and research. Technological development in areas such as machine learning opens the door to new approaches in data analysis and data knowledge extrapolation. What we propose in this work is a new approach in system identification that takes the advantage of the newest Deep Learning research for the virtualization of models from measurements. NeuralODEs are a new formulation of the classical Neural Network. NeuralODE set themselves the objective not of imitating the pattern of a system but really learning its dynamics from data.