Abstract: A computer-implemented method for operating a laser material processing machine. Process parameters are varied with the aid of Bayesian optimization until a result of the manufacturing, in particular the laser material processing, is sufficiently good. The Bayesian optimization is carried out with the aid of a data-based process model in a first phase, the data-based process model being trained as a function of estimated results. In a second phase, the data-based process model is trained as a function of the ascertained result resulting upon activation of the laser material processing machine.

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 setting operating parameters of a system, in particular, a manufacturing machine, with the aid of Bayesian optimization of a data-based model, which (in the Bayesian optimization) is trained to output a model output variable, which characterizes an operating mode of the system, as a function of the operating parameters. The training of the data-based model takes place as a function of at least one experimentally ascertained measured variable of the system and the training also taking place as a function of at least one simulatively ascertained simulation variable. The measured variable and the simulation variable each characterize the operating mode of the system. The measured variable and/or the simulation variable is transformed during training with the aid of an affine transformation.

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 computer-implemented method for operating a laser material processing machine. Process parameters are varied with the aid of Bayesian optimization until a result of the laser material processing is sufficiently good. The Bayesian optimization taking place with the aid of a data-based process model, and it being taken into consideration during the variation of the process parameters how probable it is that a variable which characterizes a quality of the result is within predefinable boundaries.

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 training a data-based model to ascertain an energy input of a laser welding machine into a workpiece as a function of operating parameters of the laser welding machine. The training is carried out as a function of an ascertained number of spatters.

Abstract: A method for setting operating parameters of a system, in particular, a manufacturing machine, with the aid of Bayesian optimization of a data-based model, which (in the Bayesian optimization) is trained to output a model output variable, which characterizes an operating mode of the system, as a function of the operating parameters. The training of the data-based model takes place as a function of at least one experimentally ascertained measured variable of the system and the training also taking place as a function of at least one simulatively ascertained simulation variable. The measured variable and the simulation variable each characterize the operating mode of the system. The measured variable and/or the simulation variable is transformed during training with the aid of an affine transformation.

Abstract: The invention relates to coherent single photon sources that provide photons with a high degree of indistinguishability. It is a disadvantage of single photon sources based on QDs in nanophotonic structures that, even at low temperatures, acoustic vibrations interact with the QDs to reduce the coherence of the emitted spectrum. The invention uses mechanical clamping of the nanostructure to damp vibrations leading to a weaker QD—phonon coupling and a higher degree of indistinguishability between successively emitted photons. The clamp is mechanically connected to the length of the photonic nanostructure and has a stiffness and a size sufficient to suppress low frequency vibrations (??10 GHz) in a combined structure of the clamp and the nanostructure.

Abstract: A computer-implemented method for operating a laser material processing machine. Process parameters are varied with the aid of Bayesian optimization until a result of the laser material processing is sufficiently good. The Bayesian optimization taking place with the aid of a data-based process model, and it being taken into consideration during the variation of the process parameters how probable it is that a variable which characterizes a quality of the result is within predefinable boundaries.

Abstract: A computer-implemented method for operating a laser material processing machine. Process parameters are varied with the aid of Bayesian optimization until a result of the manufacturing, in particular the laser material processing, is sufficiently good. The Bayesian optimization is carried out with the aid of a data-based process model in a first phase, the data-based process model being trained as a function of estimated results. In a second phase, the data-based process model is trained as a function of the ascertained result resulting upon activation of the laser material processing machine.

Abstract: The invention relates to coherent single photon sources that provide photons with a high degree of indistinguishability. It is a disadvantage of single photon sources based on QDs in nanophotonic structures that, even at low temperatures, acoustic vibrations interact with the QDs to reduce the coherence of the emitted spectrum. The invention uses mechanical clamping of the nanostructure to damp vibrations leading to a weaker QD—phonon coupling and a higher degree of indistinguishability between successively emitted photons. The clamp is mechanically connected to the length of the photonic nanostructure and has a stiffness and a size sufficient to suppress low frequency vibrations (??10 GHz) in a combined structure of the clamp and the nanostructure.