Structure-preserving super-resolution neural architecture. First, an encoder is used to reduce the dimensionality of the problem, obtaining a set of reduced variables or latent code. Then, a structure-preserving neural network (SPNN) is trained to integrate the time evolution of the reduced variables of the system. Finally, the decoder is used to recover the data to its original dimensionality and to generate the output in a resolution that is higher than the input one. Reproduced from [313].