1. A method for the computer-assisted modeling of a technical system, comprising: for a plurality of operating points, describing the operation of the technical system by a first state vector with one or more first state variables and by a second state vector with one or more second state variables; modeling the technical system training a neural network comprising at least one feed-forward network based on training data from known first and second state vectors for a plurality of operating points; wherein the at least one feed-forward network contains a plurality of neural layers comprising an input layer, at least one hidden layer and an output layer, wherein the neural layers are interconnected by connectors with respective weights and the input layer is linked with at least one first state vector and the output layer with at least one second state vector; wherein at least one connector of the at least one feed-forward network is a bridging connector which connects a neural layer with the output layer while bridging at least one hidden layer; and wherein the at least one feed-forward network comprises a plurality of hidden layers and each hidden layer not connected directly with the output layer is connected with the output layer by a bridging connector, wherein the neural network furthermore contains a recurrent neural network, coupled with the at least one feed-forward network, comprising an input layer and a recurrent hidden layer, wherein the input layer of the recurrent neural network comprises first state vectors at chronologically sequential operating points of the technical system comprising one current and one or more past operating points, wherein each first state vector at the respective operating point of the input layer of the recurrent neural network is connected by way of a connector with corresponding weight with a hidden state vector at the respective same operating point of the recurrent hidden layer of the recurrent neural network, and wherein the hidden state vector at the current operating point represents the input layer of the at least one feed-forward network and the output layer of said feed-forward network represents the second state vector at the current operating point.