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: 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: 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: 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.

Abstract: Systems and methods of determining ligament attachment areas with respect to the location of a rotational axis of a joint implant are disclosed. Certain embodiments provide a computer-implemented method of evaluating placement of a joint implant with respect to a joint. The method includes determining one or more ligament attachment regions of the joint. The method further includes determining one or more rotational axes of the joint implant. The method further includes determining a position of the joint implant relative to the joint. The method further includes determining whether the one or more rotational axes align with the one or more ligament attachment regions when the joint implant is at the position. The method further includes displaying to a user an indication of whether the one or more rotational axes align with the one or more ligament attachment regions when the joint implant is at the position.

Abstract: A system and method for improved occupancy simulations. A method includes executing a scheduler algorithm based on input data in order to output a plurality of synthetic schedules, wherein the input data includes space data, persona data, and work pattern data, wherein the scheduler algorithm includes a plurality of sub-algorithms executed according to an order of priority, wherein at least an output of one of the plurality of sub-algorithms is input to another of the plurality of sub-algorithms; and inputting the plurality of synthetic schedules to a simulation engine, wherein the simulation engine runs a plurality of simulations using the plurality of synthetic schedules.

Abstract: Sample delta monitoring is described herein. In one or more implementations, first and second experimental groups are defined in a randomized controlled trial. A first set of data indicative of a first experimental group in the randomized controlled trial is received. A second set of data indicative of a second experimental group in the randomized controlled trial is received. Based on sample counts of the first and second sets of data, an indication is generated that the experimental groups in the randomized controlled trial are skewed.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to generate a predictive asset health quantifier of a turbine engine. An example apparatus includes a performance model analyzer to determine a fleet behavior parameter by generating a reference performance model using historical information for a fleet of operators using turbine engines, generate a residual performance model based on calculating a difference between the fleet behavior parameter and a plurality of operator behavior parameters, identify an operator as a candidate improvement target based on comparing the operator behavior parameters corresponding to the operator to the fleet in the residual performance model, and determine an adjusted operator behavior parameter for the candidate improvement target.

Abstract: A target design is produced with a computer-implemented method that takes into consideration an astigmatic effect and is suitable for optimizing a spectacle lens having the astigmatic effect. The method includes providing an initial target design that does not directly take into consideration the astigmatic effect of the spectacle lens, and producing the target design by correcting the initial target design on the basis of a correction target design that directly takes into consideration the astigmatic effect of the spectacle lens.

Abstract: A field programmable gate array including a configurable interconnect fabric connecting logic blocks implementing a circuit to: receive input data including data values organized into rows and columns, each row having N data values; select R[i] unmasked data values of a row of the input data in accordance with a mask and an index i of the row; select N?[i] unmasked data values of another row of the input data in accordance with the mask and an index of the another row; merge the R[i] unmasked data values of the row and the N?[i] data values of the another row into a combined data vector of N data values; and compute R[i] normalized values based on the R[i] unmasked data values of the combined data vector and N?[i] normalized values based on the N?[i] data values of the combined data vector to generate N normalized data values.

Abstract: Systems, methods, and computer-readable media are disclosed for a quick evaluation system for three-dimensional (3D) assets used in simulations for an autonomous vehicle (AV). A disclosed method comprises receiving drive data recorded in a physical environment by a vehicle having a first sensor; simulating the first sensor associated with a virtual autonomous vehicle in a virtual environment of a 3D scene including an object that at least partially corresponds to the physical environment; evaluating simulated data based on the simulation of the first sensor using a machine learning (ML) model; comparing evaluation data recorded during the evaluation of the simulation of the first sensor using the ML model to the drive data recorded in the physical environment; and generating a report based on a comparison of the evaluation data to a portion of the drive data to determine metrics associated with the object in the virtual environment.

Abstract: A method comprises: obtaining object model data defining an object or objects to be built by a three-dimensional printing apparatus; determining an effect of carrying out a post-build process on the object or objects, wherein the determining uses the object model data and a computer-implemented evaluation of the post-build process and is carried out prior to building the object or objects, to identify portions of the object or objects that would not be processable by the cleaning process; and outputting an indication of non-processability of portions of the object or objects are determined not to be processable by the post-build process.

Abstract: In sensor data analytics, physics-based models generate high quality data. However, these models consume lot of time as they rely on physical simulations. On the other hand, generative learning takes much less time to generate data, and may be prone to error. Present disclosure provides system and method for generation of synthetic machine data for healthy and abnormal condition using hybrid of physics based and generative model-based approach. Finite Element Analysis (FEA) is used for simulating healthy and faulty parts in machinery with set of parameters and pre-condition(s). Small output data from FEA is fed into a generative model for generating synthesized data by learning data distribution knowledge and representing into latent space. Rule engine is built using statistical features wherein realistic bounds serve as faulty data indicators. Synthesized data which does not satisfies features bounds are discarded. Further, AI-based validation framework is used to analyze quality of synthesized data.

Abstract: A method of predicting wafer defect information includes estimating a distribution with respect to defect occurrence time data, the defect occurrence time data including information about a time associated with a wafer defect occurrence, distinguishing a defect distribution type according to a result of the estimating the distribution, and outputting wafer defect information predicted according to the distinguished defect distribution type.

Abstract: In some examples, design parameter data for an object can be received based on user input. A set of design criteria for the object can be received based on the design parameter data. A search of a learned design virtual reality (VR) database can be implemented to identify a set of candidate designs for the object based on the set of design criteria. The learned design VR database can include a plurality of previously determined designs for the object. Each candidate design for the object can be simulated in a simulation environment based on a learned design simulation database and optimization criteria to identify at least one new design for the object. A ranked design list can be generated ranking each candidate design and the at least one new design for the object based on ranking criteria.

Abstract: A method of and a system for propagating a configuration setting of a 3D model to other 3D models, the 3D model and the other 3Ds being part of a same 3D model category. The 5 method comprises dividing the 3D model into a first plurality of surfaces; associating to each one of the first plurality of surfaces, a unique surface identifier. For each one of the other 3D models, the method comprises dividing the one of the other 3D models into a second plurality of surfaces; establishing a correspondence; associating, for each one of the second plurality of surfaces corresponding to one of the first plurality of surfaces. The method also comprises 0 selecting the configuration setting of the 3D model; determining the unique identifier of the 3D model to which the configuration setting is to be applied; and propagating, the configuration setting to the other 3D models.

Abstract: A method of determining the orientation of a 3D Model to be generated by an additive manufacturing apparatus having a vat for holding photocurable material; and a platform for holding the 3D object corresponding to the 3D model. The platform is relatively movable with respect to the vat and the method includes a step of defining the surface geometry of the 3D model and the surface geometry includes surface segments si.

Abstract: A method for real-time simulation of an electric motor includes: modeling, by a computer system, the electrical motor in a graphical model including at least two blocks connected by signals. The graphical model includes at least two matrix operations which produce a multidimensional output signal from at least one multidimensional input signal. The computer system includes an operator computer and a real-time computer connected thereto, wherein the operator computer executes a graphical modeling environment, and the real-time computer has at least one programmable logic device.