International audienceAs more and more data is available on a daily basis, it is natural to seek to infer relevant information embedded in these datasets. In the context of dynamical systems, this idea translates into the use of observations (data) to infer the evolution law. I this sense, machine learning techniques are getting more space in the analysis and synthesis of dynamical systems. This paper uses regularized data-driven regression algorithm to infer the evolution law of a Duffing oscillator, using a library of mathematical functions obtained from a dataset generated from the underlying physical system
