Formulation of a new methodology that combines machine learning and data assimilation techniques. The methodology consists in using an Autoencoder to reduce the size of the input. In the latent space, a recurrent neural network (LSTM) is used as a surrogate for a dynamic system. The accuracy of the model is improved by using the Kalman Filter in the latent space which incorporates data (observation) collected by sensors, producing the updated state. The updated state is then reported in the original physical space by the decoder. The methodology was applied to a real test case
