Abstract: 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.

Abstract: System and method for logic rule formula induction on knowledge graphs for engineering system designs include receiving plurality of knowledge graphs for an engineering system. For a disconnected knowledge graph, agglomerative beam search is constrained to edges connected from node of interest, and candidate formulas are generated representing a respective edge found by the beam search engine, each formula constrained by a requirement of at least two arguments for defined formula chain length. Formula evaluation establishes whether each candidate formula is valid. Top ranked formulas are selected from the candidate formulas according to defined criteria. For well-connected graphs, a graph neural network is trained to predict first class for a query graph and second class for distractor graph. Counterfactual solver engine solves for minimum number of edits to query graph toward distractor graph which transforms predicted first class of the query graph to predicted second class.

Abstract: System and method optimize recyclability of an electronic device during manufacturing design A manufacturing design software produces engineering bill of materials, manufacturing bill of materials, and bill of process for the manufacturing design. Recycling process plan engine constructs a recycling process plan for the electronic device according to the manufacturing design. A virtual model of recycling processes is constructed by mapping needed skills to corresponding recycling equipment using a library of recycling equipment information. Recycling process plan engine uses the virtual model to simulate the recycling plan and optimizes each recycling process, and the overall sequence of recycling processes, according to an objective function. Evaluator module receives key performance indicator values from a virtual model simulation and calculates the value of the objective function based on key performance indicators.

Abstract: A method for automatically determining of a changed manufacturing process of a product with changed manufacturing process data, including providing basic data which include historical manufacturing process data of at least one historical manufacturing process of the product, historical product data of the product, target manufacturing process data of a planned manufacturing process or target product data of a planned product classifying the basic data determining the changed manufacturing process with the aid of the classified data, wherein the providing, the classifying, and/or the determining of the changed manufacturing process is carried out with the aid of graph technology. In addition to the method, an apparatus with a computer system for carrying out of the method is provided. The computer system includes at least one insighter engine for the executing of the method and the insighter engine includes at least one data providing tool for providing of the basic data.

Abstract: System and method for latent bias detection by artificial intelligence modeling of human decision making using time series prediction data and events data of survey participants along with personal characteristics data for the participants. A deep Bayesian model solves for a bias distribution that fits a modeled prediction distribution of time series event data and personal characteristics data to a prediction probability distribution derived by a recurrent neural network. Sets of group bias clusters are evaluated for key features of related personal characteristics. Causal graphs are defined from dependency graphs of the key features. Bias explainability is inferred by perturbation in the deep Bayesian model of a subset of features from the causal graph, determining which causal relationships are most sensitive to alter group membership of participants.

Abstract: Systems and methods are provided for generating a flow control plan for a plurality of components in a flow control system. A decentralized multi-agent control framework is used to plan and schedule for each agent independently without a central processor. Each agent of the multi-agent control framework separately optimizes a local portion of the system as a function of values for one or more parameters. Agents communicate with other connected agents, sharing values for parameters. The communication provides a negotiation and consensus for values of the shared parameters that are used by the agent to recalculate optimized parameters values for the local portion of the system.

Abstract: A system for autonomous generative design in a system having a digital twin graph a requirements distillation tool for receiving requirements documents of a system in human-readable format and importing useful information contained in the requirements documents into the digital twin graph, and a synthesis and analysis tool in communication with the digital twin graph, wherein the synthesis and analysis tool generates a set of design alternatives based on the captured interactions of the user with the design tool and the imported useful information from the requirements documents. The system may include includes a design tool with an observer for capturing interactions of a user with the design tool, In addition to the observer, an insighter in communication with the design tool and with the digital twin graph receives design alternatives from the digital twin graph and present the receive design alternatives to a user via design tool.

Abstract: A method of optimizing an additive manufacturing (AM) process includes receiving at least one design parameter of the AM process, receiving information relating to uncertainty in at least one other parameter of the AM process, performing uncertainty quantification in the optimization processor based on the at least one design parameters and uncertainty information to identify a shape error in an object being produced, updating the at least one design parameter of the AM process and utilizing the updated at least one design parameter in the AM process. A system for optimizing an AM process includes a design processor to produce at least one design parameter for an object to be manufactured, and an optimization processor to receive the at least one design parameter and uncertainty information to identify a shape error in the object to be manufactured and update the design parameters based on the shape error, prior or during the manufacturing process.

Abstract: A computer-implemented method and apparatus for generating a design for a technical system or a product is provided. Depending on a set of first parameters, specifying physical properties, and second parameters, specifying perceptible properties of the technical system or product, a design is generated for the technical system or product. A performance indicator that evaluates a physical performance of the generated design is obtained. The generated design of the technical system or product is presented to a user and perception data in response to the presentation of the generated design are measured by a perception capturing unit and a perception evaluation indicator is deduced from the measured perception data. An optimized design is determined by iteratively optimizing the performance indicator and/or the perception evaluation indicator by an optimization algorithm. The method and apparatus enable an autonomous closed design loop taking human perception into account.

Abstract: A computer-implemented method for quantifying assurance of a software system includes collecting artifacts of the software system generated during phases of the software system's engineering lifecycle. A graph of graphs (GoG) is constructed encoding the artifacts. Each subgraph in the GoG is a semantic network corresponding to a distinct assurance requirement. The GoG is used to calculate a component assurance value for each software component for each distinct assurance requirement. A system assurance value is calculated based on the component assurance values. An architectural view of the software system is presented showing at least one of the component assurance values and the system assurance values.

Abstract: A computer-implemented method for detecting cyber-attacks affecting a computing device includes retrieving a plurality of sensor datasets from a plurality of sensors, each sensor dataset corresponding to involuntary emissions from the computing device in a particular modality and extracting a plurality of features from the plurality of sensor datasets. One or more statistical models are applied to the plurality of features to identify one or more events related to the computing device. Additionally, a domain-specific ontology is applied to designate each of the one or more events as benign, failure, or a cyber-attack.

Abstract: A system and method is provided that facilitates threat impact characterization. The system may include a replica programmable logic controller (PLC) that corresponds to a production PLC in a production system and that may be configured to operate at an accelerated processing speed that is at least two times faster than a processing speed of the production PLC. The system may also include a data processing system configured to communicate with the replica PLC when executing malware infected PLC firmware and generate a simulation of the production system based on a virtual model of the production system operating at an accelerated processing speed that is at least two times faster than a processing speed of the physical production system. The simulation may include accelerated simulation of the production PLC based on communication with the replica PLC using the malware infected PLC firmware.

Abstract: Machine-learned networks provide generative design. Rather than emulate the typical human design process, an inverse model is machine trained to generate a design from requirements. A simulation model is machine trained to recover performance relative to the requirements for generated designs. These two machine-trained models are used in an optimization that creates further designs from the inverse model output design and tests those designs with the simulation model. The use of machine-trained models in this loop for exploring many different designs decreases the time to explore, so may result in a more optimal design or better starting designs for the design engineer.

Abstract: A system and a method that enable a digital thread-driven sustainability design wherein a Digital Thread (DT) is proposed as a distributed enterprise software platform that is designed for managing lifecycle sustainability data of a product throughout its lifecycle. A digitally integrated total lifecycle product design using a Digital Thread model is provided that enables one to perform predictive computational modeling for multi-lifecycle product design. The Digital Thread enables a set of predictive computational modeling tools for total lifecycle product design optimization, simulation and uncertainty and risk analysis integrated to access data through the Digital Thread. A systematic approach for development and analysis of a lifecycle sustainability model of a designed product is provided. Also, a central repository concept or a single point of access to the lifecycle sustainability data is provided.

Abstract: A computing system may include a data access engine and a toolpath adjustment engine. The data access engine may be configured to access a computer-aided design (CAD) model of a part design and a computer-aided manufacturing (CAM) setup for the part design. The CAM setup may include a nominal toolpath specified through the CAD model for performing a finishing operation for the part design. The data access engine may also be configured to obtain 3-dimensional (3D) scan data for a physical part manufactured from the part design. The toolpath adjustment engine may be configured to extract, from the 3D scan data, a manufactured geometry of the physical part manufactured from the part design and generate an adjusted toolpath for the physical part to account for the manufactured geometry extracted from the 3D scan data.

Abstract: A method of optimizing an additive manufacturing (AM) process includes receiving at least one design parameter of the AM process, receiving information relating to uncertainty in at least one other parameter of the AM process, performing uncertainty quantification in the optimization processor based on the at least one design parameters and uncertainty information to identify a shape error in an object being produced, updating the at least one design parameter of the AM process and utilizing the updated at least one design parameter in the AM process. A system for optimizing an AM process includes a design processor to produce at least one design parameter for an object to be manufactured, and an optimization processor to receive the at least one design parameter and uncertainty information to identify a shape error in the object to be manufactured and update the design parameters based on the shape error, prior or during the manufacturing process.

Abstract: A system for autonomous generative design in a system having a digital twin graph a requirements distillation tool for receiving requirements documents of a system in human-readable format and importing useful information contained in the requirements documents into the digital twin graph, and a synthesis and analysis tool in communication with the digital twin graph, wherein the synthesis and analysis tool generates a set of design alternatives based on the captured interactions of the user with the design tool and the imported useful information from the requirements documents. The system may include includes a design tool with an observer for capturing interactions of a user with the design tool, In addition to the observer, an insighter in communication with the design tool and with the digital twin graph receives design alternatives from the digital twin graph and present the receive design alternatives to a user via design tool.

Abstract: Systems (500) and methods (400) for an interactive system for automatic generation, analysis and exploration of composable system of systems based on knowledge graphs. A method (400) includes receiving (405) a scenario (110) and a domain ontology (111); determining (410) structures (132), attributes (133), and capabilities (131) from the domain ontology; generating (415) design alternatives (146) based on the scenario using the structures, attributes, and capabilities; performing (430) an evaluation (159) of the design alternatives based on the scenario; generating (445) an SoS design (300) based on the evaluation; and displaying the SoS design to a user.

Abstract: 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.

Abstract: A method, and corresponding systems and computer-readable mediums, for implementing a hierarchical multi-agent control system for an environment. A method includes generating an observation of an environment by a first agent process and sending a first message that includes the observation to a meta-agent process. The method includes receiving a second message that includes a goal, by the first agent process and from the meta-agent process. The method includes evaluating a plurality of actions, by the first agent process and based on the goal, to determine a selected action. The method includes applying the selected action to the environment by the first agent process.