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 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 operating a gas sensor system comprising a gas sensor, in order to provide a concentration variable of a gas concentration of a gas component in a sample gas. The method includes: measuring the gas concentration during a measurement process in order to obtain a temporal evolution of a sensor signal as a function of the gas concentration; determining the concentration variable using a data-based sensor model as a function of the temporal evolution of the sensor signal, the data-based sensor model being trained to take into account a behavior of the sensor outside the measurement process in order to ascertain the concentration variable.

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.