Abstract: A machine controller, geometry data and measured physical data of a machine is provided. The geometry data and the physical data are input to a machine learning module and to a simulation module of the machine controller. By the input data, the simulation module generates first values of a first physical property of a component of the machine on a discretized grid. Furthermore, an evaluator is provided for evaluating a physical compatibility of the first values with second values of a second physical property of the component, and for generating a residual quantifying the compatibility. The evaluator evaluates the compatibility of the first values with output data of the machine learning module and generates a resulting residual. Moreover, the machine learning module is trained to minimize the resulting residual, thus configuring the machine controller for controlling the machine by the output data of the trained machine learning module.

Abstract: In order to be able to take into account machining configurations more flexibly, a method for optimizing numerically controlled machining of a workpiece includes ascertaining geometric interaction data. A relationship between a force to be expected and a configuration parameter of the machining is determined on the basis of the interaction data. The force is calculated during the machining on the basis of the relationship and a current value of the at least one configuration parameter. The machining is adapted depending on the calculated force.

Abstract: A method for operating a numerical controlled machine comprising receiving a sequence of control commands which, when executed by a numerical controlled machine, cause the numerical controlled machine to machine a workpiece to obtain a predetermined workpiece geometry, wherein the sequence of control commands includes while machining the workpiece based on the received sequence of control commands measuring a value of a first interaction parameter for a first position of the tool, comparing a measured value of the first interaction parameter for the first position of the tool with the simulated value of the first interaction parameter for the first position of the tool, and determining an adapted value of the second interaction parameter for a following position of the tool based on a result of the comparison.

Abstract: Provided is a method for the computerized control of an end element of a machine tool. The method includes a method step of sensing a plurality of optical markers in a work environment of the machine tool by means of an optical measuring system. The method includes a method step of determining a first relative pose between the end element and a workpiece on the basis of the plurality of sensed optical markers. The method includes a method step of determining a first correction value on the basis of a comparison of the first relative pose with a reference pose. The method includes a method step of controlling the end element for machining the workpiece taking the first correction value into consideration.

Abstract: A method which includes steps of providing a state space model of behaviour of a physical system, the model including covariances for state transition and measurement errors, providing a data based regression model for prediction of state variables of the physical system, observing a state vector comprising state variables of the physical system, determining a prediction vector of state variables based on the state vector, using the regression model, and combining information from the state space model with predictions from the regression model through a Bayesian filter, is provided.

Abstract: In order to be able to take into account machining configurations more flexibly, a method for optimizing numerically controlled machining of a workpiece includes ascertaining geometric interaction data. A relationship between a force to be expected and a configuration parameter of the machining is determined on the basis of the interaction data. The force is calculated during the machining on the basis of the relationship and a current value of the at least one configuration parameter. The machining is adapted depending on the calculated force.

Abstract: A system, a control unit, and a method for controlling operation of a technical system are provided. The technical system includes a plurality of sensors. The method includes receiving first sensor data from a first sensor of the plurality of sensors. The method includes detecting a first sensor anomaly based on failure of the first sensor to generate the first sensor data. The failure of the first sensor includes generation of anomalous first sensor data. The method also includes validating the first sensor anomaly based on a comparison between the first sensor data and a virtual first sensor data. Thereafter, a control command is generated to the technical system by replacing the virtual first sensor data in lieu of the first sensor data when the first sensor anomaly is validated.

Abstract: A method using machine learned, scenario based control heuristics including: providing a simulation model for predicting a system state vector of the dynamical system in time based on a current scenario parameter vector and a control vector; using a Model Predictive Control, MPC, algorithm to provide the control vector during a simulation of the dynamical system using the simulation model for different scenario parameter vectors and initial system state vectors; calculating a scenario parameter vector and initial system state vector a resulting optimal control value by the MPC algorithm; generating machine learned control heuristics approximating the relationship between the corresponding scenario parameter vector and the initial system state vector for the resulting optimal control value using a machine learning algorithm; and using the generated machine learned control heuristics to control the complex dynamical system modelled by the simulation model.

Abstract: A method for computer-aided control of an automation system is provided by use of a digital simulation model which simulates the automation system and which is specified by a number parameters comprising a number of configuration parameters) describing the configuration of the automation system and a number of state parameters describing the operational state of the automation system. Simulated operation runs of the automation system based on the simulation model can be performed with the aid of a computer, where a simulation run predicts a number of performance parameters of the automation system.

Abstract: Provided is a method for the computerized control of an end element of a machine tool. The method includes a method step of sensing a plurality of optical markers in a work environment of the machine tool by means of an optical measuring system. The method includes a method step of determining a first relative pose between the end element and a workpiece on the basis of the plurality of sensed optical markers. The method includes a method step of determining a first correction value on the basis of a comparison of the first relative pose with a reference pose. The method includes a method step of controlling the end element for machining the workpiece taking the first correction value into consideration.

Abstract: A method which includes steps of providing a state space model of behaviour of a physical system, the model including covariances for state transition and measurement errors, providing a data based regression model for prediction of state variables of the physical system, observing a state vector comprising state variables of the physical system, determining a prediction vector of state variables based on the state vector, using the regression model, and combining information from the state space model with predictions from the regression model through a Bayesian filter, is provided.

Abstract: A method for performing an optimized control of a complex dynamical system using machine learned, scenario based control heuristics including: providing a simulation model for predicting a system state vector of the dynamical system in time based on a current scenario parameter vector and a control vector; using a Model Predictive Control, MPC, algorithm to provide the control vector during a simulation of the dynamical system using the simulation model for different scenario parameter vectors and initial system state vectors; calculating a scenario parameter vector and initial system state vector a resulting optimal control value by the MPC algorithm; generating machine learned control heuristics approximating the relationship between the corresponding scenario parameter vector and the initial system state vector for the resulting optimal control value using a machine learning algorithm; and using the generated machine learned control heuristics to control the complex dynamical system modelled by the simulat

Abstract: A system, a control unit, and a method for controlling operation of a technical system are provided. The technical system includes a plurality of sensors. The method includes receiving first sensor data from a first sensor of the plurality of sensors. The method includes detecting a first sensor anomaly based on failure of the first sensor to generate the first sensor data. The failure of the first sensor includes generation of anomalous first sensor data. The method also includes validating the first sensor anomaly based on a comparison between the first sensor data and a virtual first sensor data. Thereafter, a control command is generated to the technical system by replacing the virtual first sensor data in lieu of the first sensor data when the first sensor anomaly is validated.

Abstract: A method includes the following steps: observing a first state vector including state variables in a physical system A; determining a first prediction vector based on the first state vector, with a data driven model for system A; determining a second prediction vector based on the first state vector, with a physics based model for system A; training a prediction fusion operator to determine a third prediction vector based on the first and second prediction vectors; validating the prediction fusion operator on the third prediction vector and another first state vector, the other first state vector concerning the same time as the third prediction vector.

Abstract: A method for computer-aided control of an automation system is provided by use of a digital simulation model which simulates the automation system and which is specified by a number parameters comprising a number of configuration parameters) describing the configuration of the automation system and a number of state parameters describing the operational state of the automation system. Simulated operation runs of the automation system based on the simulation model can be performed with the aid of a computer, where a simulation run predicts a number of performance parameters of the automation system.

Abstract: Method for generating boundary conditions for at least one model for the simulation of at least one civil infrastructure, said method comprising the process steps: (a) mapping of spatially distributed installations connected to the at least one civil infrastructure onto a data structure; (b) typification of the spatially distributed installations; and (c) determination of boundary conditions for the at least one model by means of the spatially distributed installations that have been typified.

Abstract: A city lifecycle management system for providing the means for city stakeholders to measure the performance of their decisions against defined key performance indicators with respect to a sustainable development of an urban area, said city lifecycle management system comprising: a data and software platform enabling a collaborative creation and consistent management of city data of said urban area; a modelling and simulation framework using said data and software platform, wherein said modelling and simulation framework comprises software modules which evaluate interactions between city objects of one and/or different disciplines; and an application layer comprising application programs being adapted to derive the key performance indicators depending on the interactions evaluated by said software modules of said modelling and simulation framework, wherein said derived key performance indicators support said city stakeholders in making decision with respect to the sustainable development of the respective ur