A system and method for detecting, based on a simulation of a build of an object using additive manufacturing, if the build of the object would be flawed or would fail during actual additive manufacturing of the object is provided.

A parallel analog circuit automatic optimization method based on genetic algorithm and machine learning comprises global optimization based on genetic algorithm and local optimization based on machine learning, with the global optimization and the local optimization performed alternately. The global optimization based on genetic algorithm utilizes parallel SPICE simulations to improve the optimization efficiency while guaranteeing the optimization accuracy, combined with parallel computing. The local optimization based on machine learning establishes a machine learning model near the global optimal point obtained by the global optimization, and uses the machine learning model to replace the SPICE simulator, thus reducing the time costs brought by a large number of simulations.

In an example, a method includes obtaining a compensation model characterising a relationship between a location of an object within a fabrication chamber of an additive manufacturing apparatus and a geometrical compensation to be applied to a model of said object, wherein different geometrical compensation values are associated with different locations. In some examples the method further includes determining a magnitude of a dimension parameter of each object of a set of objects to be generated in a build operation. The method may include determining a spatial arrangement of objects to be generated within the build volume, based on the magnitude of the dimension parameters and the geometrical compensation values for an intended location of object generation in the spatial arrangement.

A method for simulating a layout of an integrated circuit manufactured by a semiconductor process includes extracting a plurality of pattern layouts from layout data that defines the layout, generating training data by amplifying the plurality of pattern layouts and at least one parameter provided from the semiconductor process, generating sample data by sampling the training data, generating feature data including a three-dimensional array from the sample data, providing the sample data and the feature data to a simulator and a machine learning model, respectively, and training the machine learning model based on an output of the machine learning model and an output of the simulator.

A system, method, and computer program product for predicting mismatch contribution in an electronic environment. Embodiments may include modeling, using a processor, a discrete output mismatch contribution problem using sparse logistic regression to generate a mismatch contribution model and applying a cross-validation approach to increase a complexity of the mismatch contribution model. Embodiments may further include computing one or more mismatch contribution values from the mismatch contribution model and defining at least one sizing constraint or determining a worst case result associated with a sampling process based upon, at least in part, the one or more mismatch contribution values.

A modeling method and an apparatus are disclosed. The method includes: obtaining a first data set of a first indicator, and determining, based on the first data set, a second indicator similar to the first indicator; and determining a first model based on one or more second models associated with the second indicator. The first model is used to detect a status of the first indicator, and the status of the first indicator includes an abnormal state or a normal state. The second models are used to detect a status of the second indicator, and the status of the second indicator includes an abnormal state or a normal state.

Techniques are disclosed for eliminating redundancy in fault simulations to improve efficiency and to reduce the time and computing power required to generate a robust fault list, which results in adequate diagnostic coverage of a particular post-silicon electronic device for functional safety applications. The techniques described herein implement an automated methodology to identify identical sub-circuits in a design after the design is synthesized to gates, and utilize isomorphism to define a manner in which identical blocks may be reliably identified to ensure adequate coverage and accurate, consistent fault injection results. The netlist may advantageously implement a “flat” as opposed to a hierarchal design. Moreover, multiple levels of granularity may be identified for the various sub-circuits associated with the reference graphs used to identify isomorphic sub-graphs.

Methods, systems, and apparatus for generating a recipe for a concrete mixture, comprising: obtaining an optical characterization of a set of particles; determining, based on the optical characterization, physical characteristics of the set of particles; generating a multispherical approximation of the set of particles; selecting, based on the physical characteristics of the set of particles and from a database of performance rules, performance rules applicable to the set of particles; predicting performance of a proposed recipe for a concrete mixture formed from the set of particles by: determining a wet flowability rating of the proposed recipe based on the selected performance rules; and determining a dry packing rating of the proposed recipe based on the multispherical approximation; iteratively altering the proposed recipe and predicting performance of the altered proposed recipe until the predicted performance satisfies performance criteria to obtain a final recipe; and outputting the final recipe.

A casting system design method is disclosed. The casting system design method comprises the steps of: receiving an input of entities associated with the shape of a cast product; generating respective entities for the constituent elements of a casting system on the basis of the inputted shape-related entities and pre-stored knowledge-based basic design information; generating a 3D graphic shape of a casting system designed on the basis of the generated entities; and editing the design of the casting system according to editing commands inputted on a graphics user interface (GUI) on which a 2D graphic shape corresponding to the generated 3D graphic shape is displayed, and dynamically modifying and displaying the 2D graphic shape so as to correspond to the editing.

Systems and methods include a computer-implemented method for generating and modeling oil viscosity profiles. An Equation-of-State (EOS) and a field pressure-volumetemperature (PVT) model are generated by reconciling historical well data received for multiple wells in a field of interest. Oil properties trends for checking logical tendencies for in-situ oil composition for local data and at initial conditions are generated using the EOS and the field PVT model, calibrated using measured lab-available oil density, and used to generate an in-situ oil composition for local data and conditions. An oil viscosity profile, generated in the field PVT model based on the oil properties trends, is calibrated and modeled in a two-dimensional PVT model using lab oil viscosity. The two-dimensional PVT model is tested using static and dynamic simulation models in terms of the EOS, compositions, composition gradient, and oil properties, including viscosity.