1. A method for the computer-assisted modeling of a technical system, comprising: modeling one or more output vectors on the basis of one or more input vectors by a learning process of a neural network (NN) based on training data comprising known input vectors and output vectors, a respective output vector comprising one or more operating variables of the technical system and a respective input vector comprising one or more input variables which influence the operating variable(s); wherein the neural network (NN) is a feed-forward network having a plurality of layers (I, H 1 , H 2 , H 3 , O) which are connected to one another and comprise an input layer (I), a plurality of hidden layers (H 1 , H 2 , H 3 ) and an output layer (O), the input layer (I) containing a number of input neurons for describing the input vector(s), and a respective hidden layer (H 1 , H 2 , H 3 ) containing a number of hidden neurons, and the output layer (O) containing a number of output neurons for describing the output vector(s); and wherein the output layer (O) comprises a plurality of output clusters (O 1 , O 2 , O 3 ) each comprising one or more output neurons, said plurality of output clusters corresponding to the plurality of hidden layers (H 1 , H 2 , H 3 ), and each output cluster (O 1 , O 2 , O 3 ) describing the same output vector and being connected to another hidden layer (H 1 , H 2 , H 3 ).