Abstract: Some embodiments provide a method of predicting a state of a system that is represented by a partial differential equation. The method comprises training a neural network for an initial state of said system to obtain a set of neural network parameters to provide a spatial representation of said system at an initial time. The method further comprises modifying said parameters for intermediate times between said initial time and a prediction time such that each modified set of parameters is used to provide a respective spatial representation of said system at each corresponding intermediate time using said neural network. The method further comprises modifying said set of parameters to provide a prediction set of parameters that is used to provide a predicted spatial representation of said system at said prediction time using said neural network.