Abstract: A system, a device and a method for controlling a physical or chemical process. The method includes: determining a second a posteriori model based on a first a posteriori model that describes the relationship between an input variable and an output variable of a process related to the physical/chemical process, the second a posteriori model describing the relationship between an input variable and an output variable of the physical or chemical process; and controlling the physical or chemical process using the second a posteriori model.

Abstract: A method for configuring a technical system. The method includes: detecting reference observations; conditioning reference system models on the reference observations detected for the reference system; detecting observations of results of the technical system to be configured for different values of the configuration parameters; adjusting an a priori model for the relationship between the values of the configuration parameters and the results provided by the technical system to the observations detected for the technical system, wherein the a priori model is formed from a weighted combination of the conditioned reference system models; ascertaining an a posteriori model for the relationship between the values of the configuration parameters and the results provided by the technical system by conditioning the adjusted a priori model on the observations detected for the technical system to be configured; and configuring the technical system using the ascertained a posteriori model.

Abstract: A method for controlling a physical system. The method includes training a neural network to output, for a plurality of tasks, a result of the task carried out, in each case in response to the input of a control configuration of the physical system and the input of a value of a task input parameter; ascertaining a control configuration for a further task with the aid of Bayesian optimization, the neural network, parameterized by the task input parameter, being used as a model for the relationship between control configuration and result; and controlling the physical system according to the control configuration to carry out the further task.

Abstract: A method for training a generator network that is configured to generate images with multiple objects. The method includes providing a set of training images, a generator network, and a discriminator network; drawing noise samples and target counts of objects; mapping, by the generator network, the noise samples and target counts of objects to generated images; randomly drawing images from a pool comprising generated images and training images; supplying the randomly drawn images to the discriminator network, thereby mapping them to a combination of: a decision whether the respective image is a training image or a generated image; optimizing discriminator parameters, optimizing generator parameters, and optimizing both the generator parameters and the discriminator parameters with the goal of improving the match between the predicted count of objects on the one hand, and the actual or target count of objects on the other hand.

Abstract: A system, a device and a method for controlling a physical or chemical process. The method includes: determining a second a posteriori model based on a first a posteriori model that describes the relationship between an input variable and an output variable of a process related to the physical/chemical process, the second a posteriori model describing the relationship between an input variable and an output variable of the physical or chemical process; and controlling the physical or chemical process using the second a posteriori model.

Abstract: A computer-implemented method for training a deterministic autoencoder. The autoencoder is configured to compress sample data representing objects and subsequently to reconstruct the sample data again, wherein the autoencoder is further configured to generate data representing additional objects. The method comprises the following steps: providing training data representing objects; and training the autoencoder on the basis of the training data, wherein the training of the autoencoder takes place on the basis of a probability distribution and a loss function, and wherein the loss function has a reconstruction term and a regularization term.

Abstract: A method for controlling a physical system. The method includes training a neural network to output, for a plurality of tasks, a result of the task carried out, in each case in response to the input of a control configuration of the physical system and the input of a value of a task input parameter; ascertaining a control configuration for a further task with the aid of Bayesian optimization, the neural network, parameterized by the task input parameter, being used as a model for the relationship between control configuration and result; and controlling the physical system according to the control configuration to carry out the further task.

Abstract: A method for training a generator network that is configured to generate images with multiple objects. The method includes providing a set of training images, a generator network, and a discriminator network; drawing noise samples and target counts of objects; mapping, by the generator network, the noise samples and target counts of objects to generated images; randomly drawing images from a pool comprising generated images and training images; supplying the randomly drawn images to the discriminator network, thereby mapping them to a combination of: a decision whether the respective image is a training image or a generated image; optimizing discriminator parameters, optimizing generator parameters, and optimizing both the generator parameters and the discriminator parameters with the goal of improving the match between the predicted count of objects on the one hand, and the actual or target count of objects on the other hand.