Abstract: A method for creating a software image of at least a part of a control device for a numerical simulation, the control device mapping an input vector of control device input variables to an output vector of control device output variables during operation. The creation of the software image is formed by an artificial neural network or a support vector machine, using an input vector of map input variables having control device input variables of interest, and using an output vector of map output variables having control device output variables of interest. The software image is trained with the aid of supervised learning or with the aid of reinforcement learning, using a plurality of training input vectors of the control device input variables of interest and, in the case of the supervised learning, also using a plurality of corresponding training output vectors of the control device output variables of interest.

Abstract: A computer-implemented method for providing a machine learning algorithm for determining similar scenarios based on scenario data of a data set of sensor data, wherein an optimization algorithm is applied to the feature representation, output by the first machine learning algorithm, of the first augmentation of the data set of sensor data, wherein the optimization algorithm approximates the feature representation, output by the second machine learning algorithm, of the second augmentation of the data set of sensor data. The invention further relates to a method for determining similar scenarios based on scenario data of a data set of sensor data and to a training controller.

Abstract: A method of reducing training data via a system having an encoder, wherein at least a portion of the training data forms a temporal sequence and is combined into a first set of training data, and the encoder maps input data to prototype feature vectors of a set of prototype feature vectors. A first input datum is received from the first set of training data, and propagated by the encoder. The input datum is assigned one or more feature vectors by the encoder, and depending on the assigned feature vectors, a defined set of prototype feature vectors is determined and assigned to the first input datum. An aggregated vector is created for the first input datum. A second aggregated vector is created for the second input datum and the first and second aggregated vectors are compared and a measure of similarity for the aggregated vectors is determined.