Abstract: A method for training a generative adversarial network. The method includes: iteratively training the generative adversarial network based on the training data, the training of the generative adversarial network including an alternating training of the generator and the discriminator based on the training data, the training of the generator including a training the generator based on the training data and results of realism assessments performed by the discriminator, and the training of the generative adversarial network in each iteration step including a generation of corresponding data by the generator, a performance of a realism assessment of the corresponding data by the discriminator, and a performance of an additional realism assessment of at least one specific feature derived from the corresponding data by the discriminator, and the performance of the additional realism assessment of at least one specific feature derived from the corresponding data including an application of a deterministic function.

Abstract: The invention relates to an apparatus for determining a temperature of a rotor of an electric machine. The apparatus comprises an interface and a computing device. Input variables which are dependent on the operation of the electric machine are received via the interface. The computing device calculates, by means of a physical model, a first contribution to the heat transfer on the rotor on the basis of at least one of the input variables. In addition, the computing device calculates, by means of an artificial intelligence model, a second contribution to the heat transfer on the rotor on the basis of at least one of the input variables. The computing device calculates the temperature of the rotor on the basis of the first contribution and the second contribution to the heat transfer on the rotor.

Abstract: A computer-implemented prediction method of making time-series predictions for controlling and/or monitoring a computer-controlled system, such as a semi-autonomous vehicle. The method uses a time series of one or more observed states. A state comprises values of measurable quantities of multiple interacting objects. Based on the observed states, values of time-invariant latent features for the multiple objects are determined, for example, according to an encoder model. A decoder model is then used to predict at least one next state. This involves applying a trained graph model to obtain a first prediction contribution based on an object's interactions with other objects, and applying a trained function to obtain a second prediction contribution based just on information about the object itself. Based on the predicted next state, output data is generated for use in controlling and/or monitoring the computer-controlled system.