Siemens Industry Software NV

A computer-implemented method of engineering design includes performing a simulation for a design under test of the design interacting in a real-world environment, in a computer processor, generating simulation data from performance of the simulation, storing the generated simulation data in a computer memory, extracting the generated simulation data to train a first inverse model neural network, and generating a plurality of design parameters from the inverse model neural network. A visualization representative of the generated plurality of design parameters for display to a user allows an expert user to evaluate the suggested design parameters and select some or all of the suggested design parameters. For existing designs more data is available for the simulation than for a new design. The inverse model for a new design may be augmented by transferring stored knowledge in a pre-existing inverse model to the model for the new design.

A computing platform for simulating an industrial system and a method of managing the simulation are disclosed. The method relates to generating at least one instance of a simulation model of the industrial system. The method includes: generating the instance using a contiguous memory allocator for a dynamic memory region associated with a first processor of the heterogenous processors; enabling a second processor to use the instance in the dynamic memory region based on a memory pointer associated with the address of a copy of the instance; and simulating the industrial system by the second processor by accessing the instance-copy.

A computer-implemented method for the simulation of a process by co-simulation by coupling more than one simulation model processing a sub-system as a component of the simulation. The method includes communicating the co-simulation step size of enhanced simulation models from the co-simulation master to the respective enhanced simulation model via an Inter Process Communication interface via the enhanced simulation models' Functional Mock-up Units for model exchange, the co-simulation master controlling the co-simulation step size via the inter process communication interface addressing the Functional Mock-up Unit for model exchange using DoStep-semantic instructions, the Functional Mock-up Unit for model exchange triggering event handling calls of the enhanced simulation model by notifying events to the enhanced simulation model initiated by the DoStep-semantic instructions received from the Inter Process Communication interface such that the co-simulation master controls or synchronizes the enhanced simulation model executions by using model exchange events.

Systems and methods for synchronizing programs for simulation of a technical system. The programs including a first simulation program and a second simulation program. The first simulation program simulates the kinematics of the technical system and the second simulation program simulates a control of the operation of the technical system. The first simulation program outputs values of a first variable at first-time points between first-time intervals according to a first frequency in virtual simulated time and the second simulation program outputs values of a second variable at second time points between second time intervals according to a second frequency in virtual simulated time, the first frequency being lower than the second frequency. Values of the first variable at second time points between two successive first-time points are determined based on an approximation, where the second simulation program uses the approximated values in order to determine values of the second variable at second time points between the two successive first-time points. An error is determined based on the absolute value of the difference between a first integral value as seen from the first simulation program and a second integral value as seen from the second simulation program. A warning is output by a user interface in case that the error exceeds a predetermined threshold.

Numerical modular simulation of a system includes: (a) disaggregating said system into at least two subunit simulation subsystems, and (b) simulating the respective subunits stepwise repeatedly generating subsystem-step-output from subsystem-step-input during a respective subsystem-time-step (SMP). To improve accuracy and performance, said method includes the additional steps: (c) transmitting subsystem-step-inputs to a receiving subsystem and simulating this subsystem over a delay-time before its subsystem-step-outputs are generated, (d) receiving connection interface variables from a sending subsystem including at least one of: numerical data, at least parameters of a data-prediction-model of said numerical data, or a data-prediction-model assigned to said numerical data, (e) predicting said numerical data by a data-prediction-model over said delay-time to obtain predicted numerical data of said interface variables provided by said sending subsystem, and (f) starting the next simulation step of said receiving subsystem generating the next subsystem-step-output from subsystem-step-input, wherein said subsystem-step-input includes said predicted numerical data.

A system and method for accelerated simulation setup includes receiving a description of a new problem for simulation, extracting input data and output data of previous simulation results, generating a representation of data based on the extracted input data and output data, and quantifying similarities between the new problem and the extracted input data and output data to identify a candidate simulation for the new problem. A machine learning component infers a solution output for the new problem based on extrapolation or interpolation of outputs of the candidate simulation, thereby conserving resources by eliminating a simulation generation and execution. Alternatively, an efficient simulation setup can be generated using the queried knowledge, input variables, and input parameters corresponding to the candidate simulation.

A computer-implemented method of generating a digital twin. To improve the efficiency of handling a digital twin the method includes the steps of: providing a digital twin modular system, the digital twin modular system being characterized by digital twin modules being adapted or being adaptable to digital twin features, the digital twin features relating to at least one of calculation properties, scope of simulation, model architecture, communication safety, accessibility, data storage, encryption functions, resource allocation, hardware requirements, providing a tuning module, wherein the tuning module includes a tuning module parameter set, wherein the tuning module parameter-set includes tuning module parameters respectively corresponding to at least one digital twin feature generating a digital twin by using the tuning module performing the steps of selecting, configuring, and combining digital twin modules of the digital twin modular system according to the tuning module parameters.

A method for generating a virtual model of a component using a computer-aided design (CAD) environment including a design database is provided. The design database is used to store designs. The CAD environment provides predefined design elements. At least some of the designs include predefined design elements. In order to increase an efficiency with which models are created, a design databank contains information from the design database relating to designs. In a design phase, the method further includes selecting at least one design element, determining designs that contain the selected design element from the design databank, and displaying information relating to at least one of the determined designs.

A computing platform for simulating an industrial system and a method of managing the simulation are disclosed. The method relates to generating at least one instance of a simulation model of the industrial system. The method includes: generating the instance using a contiguous memory allocator for a dynamic memory region associated with a first processor of the heterogenous processors; enabling a second processor to use the instance in the dynamic memory region based on a memory pointer associated with the address of a copy of the instance; and simulating the industrial system by the second processor by accessing the instance-copy.

A method and system for performing seamless analysis of geometric components in multi-domain collaborative simulation environments. A method includes generating a simulation interface object corresponding to a geometric component in a first simulation environment. The simulation interface object includes load and boundary conditions associated with the geometric component. The method includes dynamically accessing the load and boundary conditions associated with the geometric component in a second simulation environment via the simulation interface object. The first simulation environment and the second simulation environment correspond to different domains. The method includes performing analysis of the geometric component in the second simulation environment based on the load and boundary conditions. Moreover, the method includes generating results of the analysis of the geometric component on a graphical user interface associated with the second simulation environment.