Data assimilation as a learning tool to infer ordinary differential equation representations of dynamical models
Recent progress in machine learning has shown how to forecast and, to some extent, learn the dynamics of a model from its output, resorting in particular to neural networks and deep learning techniques.We will show how the same goal can be directly achieved using data assimilation techniques without leveraging on machine learning software libraries