Abstract: A system design learning device includes: at least one memory configured to store instructions; and at least one processor configured to execute the instructions to: perform, with respect to a design target system whose system requirement is shown, system design in which application of a conversion rule to a component of the design target system is iterated until a design result of the system design is obtained; determine an evaluation value for the system design based on the design result; determine an evaluation value related to conversion of each component in the system design based on the evaluation value for the system design; and learn about selection of a component to which the conversion rule is applied based on learning data including the evaluation value related to the conversion of each component.

Abstract: Systems, methods and instrumentalities are described herein for automatically devising and executing a surgical plan associated with a patient in a medical environment, e.g., under the supervision of a medical professional. The surgical plan may be devised based on images of the medical environment captured by one or more sensing devices. A processing device may determine, based on all or a first subset of the images, a patient model that may indicate a location and a shape of an anatomical structure of the patient and determine, based on all or a second subset of the images, an environment model that may indicate a three-dimensional (3D) spatial layout of the medical environment. The surgical plan may be devised based on the patient model and the environment model, and may indicate at least a movement path of a medical device towards the anatomical structure of the patient.

Abstract: The present disclosure provides a computer-implemented method for constructing a hierarchical industrial knowledge ontology, comprising generating a multi-tier semantic graph model representing an industrial facility. The graph comprises an equipment components tier defining components that compose individual pieces of equipment, an equipment tier defining physical asset instances, an equipment-to-equipment connectivity tier defining physical connectivity relationships among the equipment, a flow tier defining directional transfer relationships for process or utility media, an equipment-to-function tier defining functional characteristics at the equipment or component level, a functional group tier defining nested functional subgraphs composed of multiple equipment and flow relationships, and an operating group tier defining outcome-driven subgraphs associated with specific operational objectives or modes.

Abstract: A method including evaluating, with respect to a parameter representing remaining uncertainty of a mathematical model fitting measured data, one or more mathematical models for fitting measured data and one or more measurement sampling schemes for measuring data, against measurement data across a substrate, and identifying one or more mathematical models and/or one or more measurement sampling schemes, for which the parameter crosses a threshold.

Abstract: An apparatus for facilitating a multi-model home design, the apparatus comprising a processor and a memory containing instructions configuring the processor to receive a spatial configuration schema containing compartment data files, communicate with a facilitator model by selecting the facilitator model from a plurality of facilitator models based on the compartment data files and generating a plurality of action datums associated with the compartment data files at the facilitator model, determine a provider model using the facilitator model as a function of the plurality of action datums, and execute the spatial configuration schema by initiating the plurality of action datums using the provider model.

Abstract: A computer implemented lightweight design method including: a preliminary homogenization step for defining a material model related to an adopted manufacturing material; a subsequent optimization step for finding an optimal distribution of material density within the design domain; and a final post-processing step to find the geometry for manufacturing; wherein the preliminary homogenization step is performed for deriving the material model for a 2D/3D version of a porous material provided with circular/spherical holes in a Hexagonal Close-Packed (HCP) arrangement, and the post-processing step comprises computing position and size of the circular/spherical holes.

Abstract: An example method includes obtaining, by at least one processor, a plurality of categorised indications of deviations from expected dimensions for at least one object generated using an additive manufacturing apparatus wherein the categories comprise at least two of a first dimension type comprising dimensions which are expected to increase on application of a positive offset to object model data used to generate an object, a second dimension type comprising dimensions which are expected to decrease on application of a positive offset to the object to object model data used to generate an object, and a third dimension type comprising dimensions which are expected to be unaffected by application of an offset to object model data used to generate an object. The method may include determining a geometrical compensation model to apply to object model data for generating objects using additive manufacturing to compensate for anticipated deviations in dimensions.

Abstract: The invention refers to a computer-implemented method for creating a functional form, in particular as a basis for individually fitting an orthosis or prosthesis to a first patient, comprising the steps of receiving of anatomic structural data (ASD) of the first patient and applying a transfer algorithm to generate functional form data (FFD) for the first patient, whereby the received functional form data (FFD) forms the basis on which an individualized orthosis or prosthesis is manufactured. Furthermore, the invention refers to a data processing system and a computer-readable medium.

Abstract: An incision simulation device includes a processor, in which the processor acquires an incision line for a three-dimensional organ image showing an organ which is an incision simulation target, and specifies an incision region from the three-dimensional organ image based on an incision process position between the incision line and a maximum incision region, first distance data with the incision line as a reference, and second distance data with the maximum incision region designated for the three-dimensional organ image as a reference.

Abstract: Disclosed are various embodiments for a simulation framework that facilitates the integration and management of multiple services in a software-in-the-loop simulated environment. A simulation runtime engine can deploy multiple services which may have dependencies upon one another in a simulated environment. Network service calls sent from one simulated services to another service may be intercepted to ensure that the network service call is sent to the simulated instance of the service instead of the actual non-simulated instance of the service. In addition, system time calls are intercepted to ensure that the requested service is provided the simulation time.

Abstract: A machine learning system for predicting a time dependent property of a material system includes a processor and a memory communicably coupled to the processor. Stored in the memory is an acquisition module and a machine learning module. The machine learning module includes instructions that, when executed by the processor, cause the processor during each of one or more iterations, to train a machine learning model to learn an initial state vector, Hermitian operators encoding observables, and a Hamiltonian of a material system from the Schrödinger equation of the material system propagated in a time series. The machine learning model also predicts, based at least in part on the learned initial state vector, Hermitian operators, and Hamiltonian, at least one time dependent property of the material system at time not equal to zero.

Abstract: A computer-implemented method estimates a mass distribution of a physical product comprising a plurality of product parts. The method: a) creates a data structure comprising, for each part, data representing position and orientation of the part, its mass of the part, preferably its tolerance associated to each mass of the part, and its geometry in voxel format; b) decomposes a digital model of the physical product into a plurality of parallel slices (SL1); c) for each slice, uses the data structure for identifying a set of product parts of the data structure overlapping with the slice and determining a respective overlap rate; and d) attributes to each slice a mass value corresponding to a sum of the masses of all product parts overlapping with the slice, weighted by the respective overlap rates. A computer program product, non-transitory computer-readable data-storage medium, a computer system and a Computer Aided Design (CAD) Systems carry out such a method.

Abstract: An implant insertion tool or tools interact with a user interface provided by a computer to guide the operator to insert the implant at a desired location and trajectory. The user interface may guide an operator through a registration process, a process for positioning the tip of the implant at the desired location, and a process for aligning the implant to have the desired insertion trajectory. The implant may be a medical implant such as a K-wire that is inserted into a bone, such as the glenoid of a scapula during shoulder arthroplasty or reverse arthroplasty.

Abstract: Systems, methods, apparatus and mechanisms that iteratively generate one or more equations (conforming to one or more functions or functions types) of increasing complexity (e.g., degree or order) to provide one or more corresponding approximations of a physical System Under Study (SUS), where each approximation or “fit” provides additional information about the SUS and where the various equations may be summed to provide a final “fit” having an adequate level of accuracy.

Abstract: A method of efficiently modeling changes to mitigation of an occurrence of a rare event for a number of simulation trials includes obtaining or generating a number of sparse simulation trials of a simulation including a total number of simulation trials (x) associated with the N occurrences of a rare event, assigning fractions of 1/N to N/N to the sparse simulation trials, filtering the sparse simulation trials by the assigned fractions by a percentage corresponding to y/N to simulate a mitigation of the likelihood of failure, and outputting sparse simulation trials that are less than the percentage to statistically represent the effects of the mitigation on the total number of trials.

Abstract: A method for automatically generating a bill of process in a manufacturing system comprising: receiving design information representative of a product to be produced; iteratively performing simulations of the manufacturing system; identifying manufacturing actions based on the simulations; optimizing the identified manufacturing actions to efficiently produce the product to be produced; generating, by the manufacturing system, a bill of process for producing the product. Simulations may be performed using a digital twin of the product being produced and a digital twin of the environment. System actions are optimized using a reinforcement learning technique to automatically produce a bill of process based on the design information of the product and task specifications.

Abstract: Methods and systems are described for a computer-implemented complex adaptive systems metrology (CASM) technique for generating universally and mathematically standardized scores that quantify longitudinal evidence for either temporal-interaction scores or temporal-interaction benefit-and-harm scores to determine a quantitative significance estimate of scores for either standardized temporal-interaction scores or the standardized temporal-interaction benefit-and-harm scores.

Abstract: Methods and systems for detecting problematic floating nodes in an electronic circuit or electronic circuit design are presented. Floating node detection methods and systems use graph crawling techniques in combination with machine learning to perform an exhaustive search for floating nodes without requiring the use of expensive commercial simulation tools.

Abstract: A vehicle response predicting device comprising: a memory; and a processor coupled to the memory, wherein the processor is structured so as to, by using a convolutional neural network model that has been trained in advance, and that is for predicting response of a vehicle, and whose input is input data expressing a time series of an input to the vehicle and a characteristic of the vehicle, predict response of a vehicle that is an object of prediction, from input data expressing a time series of an input to the vehicle that is the object of prediction and a characteristic of the vehicle that is the object of prediction.

Abstract: The present invention relates to adaptive object fabrication using enriched digital twins that incorporate voxel-based metadata tailored to environmental conditions and material performance. A machine-learned artificial intelligence (AI) engine selects substitute materials and predicts failure modes based on environmental context data, such as temperature, vibration, and chemical exposure. These insights are embedded into a voxel-based model as prescriptive metadata, generating an enriched digital twin configured for precise additive manufacturing. Secure transmission, cryptographic authorization, and compliance indexing ensure controlled and traceable fabrication. Unlike prior approaches, the system transforms digital twins into machine-executable fabrication directives that adapt dynamically to deployment-specific requirements. The invention enables context-aware object replication with improved survivability, regulatory compatibility, and manufacturing efficiency.