Abstract: A method for verifying and/or validating whether a technical system fulfills a desired criterion. The technical system emits output signals based on input signals supplied to the technical system. The method includes: obtaining models for a plurality of components comprised by the technical system; obtaining a plurality of validation measurements; for each component, training a machine learning model to predict measurement outputs of the respective component based on inputs of the respective component; obtaining first test outputs from a last model based on test inputs; determining second test outputs from the machine learning model corresponding to the last model and based on the test inputs of the models; determining a discrepancy; verifying and/or validating whether the technical system fulfills the criterion.

Abstract: A method for verifying and/or validating whether a technical system fulfills a desired criterion. The technical system emits output signals based on input signals supplied to the technical system. The method includes: obtaining models for a plurality of components of the technical system; obtaining a plurality of validation measurements; for each component, training a machine learning model to predict outputs of the respective component; obtaining first test outputs from a last model based on test input; determining, second test outputs from the machine learning model corresponding to the last model and based on the test inputs of the models; determine a deviation which characterizes a difference between first test outputs determined from the last model and second test outputs determined by the machine learning model corresponding to the last model; verifying and/or validating whether the technical system fulfills the criterion.

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: A device and computer-implemented method for carrying out an experiment using a technical system or using a model of a technical system. A first set of input data points for the experiment is predefined. A second set of input data points for the experiment is determined as a function of the first set of input data points. A substitute model for the technical system is configured to determine, as a function of the second set of input data points, predictions for a result of the experiment for a first prediction statistic, which is to be expected for the second set of input data points when carrying out the experiment using the technical system or using the model for the technical system.

Abstract: Apparatus and computer-implemented method, including: presetting data points which include pairs of mutually assigned input and output of a Gaussian process; determining a positive semi-definite kernel matrix from inputs predetermined by the data points; determining an inverse of the kernel matrix depending on an estimation for an inverse of a 1-Lipschitz mapping of the kernel matrix; presetting an input for the Gaussian process; determining a prediction for an expected value of the Gaussian process, and/or a prediction for a variance of the Gaussian process; determining a probable output variable of a sensor and/or a control variable for a machine depending on at least one of the predictions.

Abstract: A computer-implemented method for training a machine learning system for generating driving profiles and/or driving routes of a vehicle including: a generator obtains first random vectors and generates first driving routes and associated first driving profiles related to the first random vectors, driving routes and respectively associated driving profiles recorded in driving mode are stored in a data base, second driving routes and respectively associated second driving profiles recorded in driving mode are selected from the database, a discriminator obtains first pairs made up of first generated driving routes and respectively associated first generated driving profiles and second pairs made up of second driving routes and respectively associated second driving profiles recorded in driving mode, the discriminator calculates outputs that characterize each pair, and a target function is optimized as a function of the outputs of the discriminator.

Abstract: A computer-implemented method for training a machine learning system to generate driving profiles of a vehicle. The method includes first travel routes are selected from a first database having travel routes, a generator of the machine learning system receives the first travel routes and generates first driving profiles for each of the first travel routes, travel routes and associated driving profiles determined during vehicle operation are stored in a second database, second travel routes and respective associated second driving profiles determined during vehicle operation are selected from the second database, a discriminator of the machine learning system receives pairs made up of one of the first travel routes with the respective associated first generated driving profile and pairs made up of second travel routes with the respective associated second driving profile determined during vehicle operation, as input variables.

Abstract: A model calculating unit for calculating a neural layer of a multilayer perceptron model having a hardwired processor core developed in hardware for calculating a definitely specified computing algorithm in coupled functional blocks. The processor core is designed to calculate, as a function of one or multiple input variables of an input variable vector, of a weighting matrix having weighting factors and an offset value specified for each neuron, an output variable for each neuron for a neural layer of a multilayer perceptron model having a number of neurons, a sum of the values of the input variables weighted by the weighting factor, determined by the neuron and the input variable, and the offset value specified for the neuron being calculated for each neuron and the result being transformed using an activation function in order to obtain the output variable for the neuron.

Abstract: A device and a method for operating a test stand. A set of measurements of input variables of a system model of a component of a machine is provided. An optimization problem is defined using a set of measurements of input variables. A gradient for solving the optimization problem is determined as a function of the set of measurements. A solution to the optimization problem, which defines a design for input data for the test stand for a measurement on the component, is determined as a function of the gradient. A measurement of output data is acquired on the component on the test stand as a function of the input data. Pairs of training input data and training output data are determined as a function of the input data and the measurement of output data. The system model for the component is trained as a function of the pairs.

Abstract: A model calculating unit for the selective calculation of an RBF model or of a neural layer of a multilayer perceptron model having a hardwired processor core designed in hardware for calculating a fixedly specified computing algorithm in coupled function blocks. The processor core is designed to calculate an output variable for an RBF model as a function of one or multiple input variables of an input variable vector, of supporting points, of length scales, of parameters specified for each supporting point, the processor core furthermore being designed to calculate an output variable for each neuron for the neural layer of the multilayer perceptron model having a number of neurons as a function of the one or the multiple input variables of the input variable vector, of a weighting matrix having weighting factors and an offset value specified for each neuron.

Abstract: A system for adapting a base classifier to one or more novel classes. The base classifier classifies an instance into a base class by extracting a feature representation from the instance using a feature extractor and matching it to class representations of the base classes. The base classifier is adapted using training data for the novel classes. Class representations of the novel classes are determined based on feature representations of instances of the novel classes. The class representations of the novel and base classes are then adapted, wherein at least one class representation of a novel class is adapted based on a class representation of a base class and at least one class representation of a base class is adapted based on a class representation of a novel class. The adapted class representations of the base and novel classes are associated with the base classifier.

Abstract: A model calculation unit for calculating a multilayer perceptron model, the model calculation unit being designed in hardware and being hardwired, including: a process or core; a memory; a DMA unit, which is designed to successively instruct the processor core to calculate a neuron layer, in each case based on input variables of an assigned input variable vector and to store the respectively resulting output variables of an output variable vector in an assigned data memory section, the data memory section for the input variable vector assigned to at least one of the neuron layers at least partially including in each case the data memory sections of at least two of the output variable vectors of two different neuron layers.

Abstract: A model calculation unit for calculating a gradient with respect to a certain input variable of input variables of a predefined input variable vector for an RBF model with the aid of a hard-wired processor core designed as hardware for calculating a fixedly predefined processing algorithm in coupled functional blocks, the processor core being designed to calculate the gradient with respect to the certain input variable for an RBF model as a function of one or multiple input variable(s) of the input variable vector of an input dimension, of a number of nodes, of length scales predefined for each node and each input dimension, and of parameters of the RBF function predefined for each node.

Abstract: To simulate automotive systems, a large number of synthetic data points characterising an aspect of the performance of a target automotive system are generator. In this way, for example, various future scenarios can be simulated and statistically evaluated. A computer-implemented method is provided for training a generative machine learning model, a computer-implemented method for generating synthetic data series using a generative machine learning model, and an associated apparatus. An associated computer program element and computer readable medium are also described.

Abstract: A method is provided for ascertaining a NOx concentration and an NH3 slip downstream from an SCR catalytic converter of an internal combustion engine of a vehicle. State variables of an internal combustion engine as first input variables and an updated NH3 fill level of the SCR catalytic converter as a second input variable cooperate with at least one machine learning algorithm or at least one stochastic model. The at least one machine learning algorithm or at least one stochastic model calculates the NOx concentration and the NH3 slip downstream from the SCR catalytic converter as a function of the first input variables and the second input variables and output the same as output variables.

Abstract: A method for ascertaining whether a series of sensor values contains an anomaly, including the following steps: providing a shapelet and at least one training data series; measuring in each case a distance between the shapelet and the training data series at a plurality of different predefinable positions of the training data series; ascertaining at least one minimal distance from the measured distances and ascertaining at least one change variable for at least one predefinable data point of the shapelet the change variable being ascertained as a function of at least one of the measured distances. A computer program, a device for carrying out the method, and a machine-readable memory element, on which the computer program is stored are also provided.

Abstract: A device and a method for operating a test stand. A set of measurements of input variables of a system model of a component of a machine is provided. An optimization problem is defined using a set of measurements of input variables. A gradient for solving the optimization problem is determined as a function of the set of measurements. A solution to the optimization problem, which defines a design for input data for the test stand for a measurement on the component, is determined as a function of the gradient. A measurement of output data is acquired on the component on the test stand as a function of the input data. Pairs of training input data and training output data are determined as a function of the input data and the measurement of output data. The system model for the component is trained as a function of the pairs.

Abstract: A device and a computer-implemented method for machine learning. A set of measurements of input variables of a system are provided. An optimization problem is defined as a function of the set of measurements of input variables and as a function of a unit sphere in a Hilbert space including a reproducing kernel. The unit sphere is defined as a function of the reproducing kernel. A solution the optimization problem is determined, which defines input data for a measurement at the system. A measurement of output data at the system is detected as a function of the input data. Pairs of training input data and training output data are determined as a function of the input data and the measurement of output data. A system model for the system is trained as a function of the pairs. The reproducing kernel is determined as a function of the system model.

Abstract: A device and a method for operating a test stand. A set of measurements of input variables of a system model of at least one component of a machine is provided. An optimization problem is defined as a function of a measure for an information content of input variables with regard to output variables which are characterized by the system model. A gradient for solving the optimization problem is determined as a function of the set of measurements of input variables. A solution to the optimization problem is determined as a function of the gradient. A measurement of output data is acquired on the at least one component of the machine on the test stand as a function of the input data. Pairs of training input data and training output data are determined as a function of the input data and the measurement of output data.

Abstract: A model calculating unit for calculating a neural layer of a multilayer perceptron model having a hardwired processor core developed in hardware for calculating a definitely specified computing algorithm in coupled functional blocks. The processor core is designed to calculate, as a function of one or multiple input variables of an input variable vector, of a weighting matrix having weighting factors and an offset value specified for each neuron, an output variable for each neuron for a neural layer of a multilayer perceptron model having a number of neurons, a sum of the values of the input variables weighted by the weighting factor, determined by the neuron and the input variable, and the offset value specified for the neuron being calculated for each neuron and the result being transformed using an activation function in order to obtain the output variable for the neuron.