1. A method for identifying, forecasting and controlling a non-linear flow on a physical system network formed by branches and nodes and representing a physical system having dynamics specified by system dynamics parameters, comprising the steps of: (a) representing the physical system network by a neural network model by dividing each branch between the nodes in the physical system network by a plurality of division points, assigning each unit of neural network to each of the division points, an initial point, and a terminal point of said each branch, and specifying neural network model parameters for indicating connections among units of the neural network; (b) calculating a flow of each of the division points and (a) the terminal point of said each branch according to the neural network model parameters specifying connections among the division points and the terminal point in a neural network model representing the physical system network; (c) measuring an actual flow in the physical system at a location of the terminal point of said each branch in the physical system network; (d) calculating an error of the flow at the terminal point calculated at step (b) with respect to the actual flow at the terminal point measured at step (c); (e) adjusting the neural network model parameters, so as to minimize the error calculated at step (d); and (f) determining the system dynamics parameters specifying the dynamics of the physical system represented by the physical system network, according to the neural network model parameters adjusted at step (e).