A design engine implements a probabilistic approach to generating designs for computer-aided design (CAD) assemblies. The design engine initially generates a population of designs based on a problem definition associated with a design problem. Each design includes a randomly-generated set of design values assigned to various design variables. The design engine repairs any infeasible designs in the population of designs and then executes a dynamic simulation with the population of designs. The design engine selects the most performant designs and identifies, based on those performant designs, design variables that are dependent on one another. The design engine generates a probability model indicating conditional probabilities between design values associated with dependent design variables. The design engine then iteratively samples the probability model to generate a subsequent population of designs. In this manner, the design engine can automatically generate designs for mechanical assemblies significantly faster than possible with conventional algorithmic design techniques.

Determining one or more device parameters (Dp) of one or more parts of an integrated circuit (IC), including: simulating the IC; measuring one or more electrical characteristics of the one or more parts of the IC; using the one or more measured electrical characteristics of the one or more parts of the IC and the simulation to determine the one or more device parameters (Dp) of the one or more parts of the IC; for each part of the IC, determining a corresponding joint probability distribution of the one or more device parameters using the simulation; using maximum likelihood (ML) techniques to determine an estimate of the one or more device parameters; and using the one or more measured electrical characteristics of the one or more parts of the IC and the simulation to improve the estimate of the one or more device parameters.

Implementations are directed to design advisor platform uses machine learning (ML) models (e.g., deep learning models, such as convolutional neural networks (CNNs)) to compare an input design with a set of reference designs and provide evaluation and recommendation of the input design on-the-fly (i.e., in real-time).

For risk evaluation, a method encodes event data as a linear array that includes a plurality of logic states. The method estimates a success probability for each logic state and identifies path groups of the plurality of logic states. The logic states of each path group must all be healthy for each logic state to contribute to system success. The method further identifies each path combination of path groups and path nodes that result in system success. In addition, the method calculates a system success probability as a sum of success probabilities for each path combination. The success rate for each path combination is calculated as a product of the path group success probabilities for the path combination.

A design engine implements a probabilistic approach to generating designs that exposes automatically-generated design knowledge to the user during operation. The design engine interactively generates successive populations of designs based on a problem definition associated with a design problem and/or a previously-generated population of designs. During the above design process, the design engine generates a design knowledge graphical user interface (GUI) that graphically exposes various types of design knowledge to the user. In particular, the design engine generates a design variable dependency GUI that visualizes various dependencies between designs variables. The design engine also generates a design evolution GUI that animates the evolution of designs across the successive design populations. Additionally, the design engine generates a design exploration GUI that facilitates the user exploring various statistical properties of automatically-generated designs.

Improved surgical planning systems and methods are provided for planning orthopaedic procedures, including pre-operatively, intra-operatively, and/or post-operatively to create, edit, execute, and/or review surgical plans. The surgical planning systems and methods may be utilized for planning and implementing orthopaedic procedures to restore functionality to a joint. In some embodiments, preoperative surgical planning may be influenced by analyzing a survivorship predictive index that is a percentile representation of a confidence level that the surgical plan will result in a successful surgical outcome for at least a predefined amount of time.

In accordance with one embodiment of the present disclosure, a method includes receiving exploration constraints, including a budget, receiving prospect inputs for a plurality of prospects, each prospect input having fixed inputs and dynamic inputs, generating correlation matrices based on the dynamic inputs, determining a set of drilling sequences for the plurality of prospects based on the budget, modeling, by Monte Carlo simulation, each drilling sequence of the set of drilling sequences within the prospect inputs, wherein each iteration of modeling is complete when the exploration constraints are reached, and generating an optimal drilling sequence, including a risk and a reward for the optimal drilling sequence.

A method of generating a mask used in fabrication of a semiconductor device includes, in part, selecting a source candidate, generating a process simulation model that includes a stochastic variance band model in response to the selected source candidate, performing a first optical proximity correction (OPC) on the data associated with the mask in response to the process simulation model, assessing one or more lithographic evaluation metrics in response to the OPC mask data, computing a cost in response to the assessed one or more lithographic evaluation metrics, and determining whether the computed cost satisfies a threshold condition. In response to the determination that the computed cost does not satisfy the threshold condition, a different source candidate may be selected.

System and method to build and score predictive model for numerical attributes are provided. The system includes a memory and a processing subsystem. The processing subsystem is configured to select one or more numerical variables from the plurality of data sets based on a plurality of parameters, to apply feature engineering and transformation on the one or more numerical variables, to perform time series forecasting on the one or more numerical variables based on the plurality of features extracted, to evaluate and select appropriate prediction technique based a regression technique based on a plurality of elements, to build a prediction model, to score the built prediction model based on the performed time series forecasting and an evaluated regression technique and to predict the built prediction model based on an obtained score. Further, the system uses the plurality of parameters and the prediction method to score and predict the prediction model.

A method of creating a fabricable segmented design for a physical device is provided. A computing system receives a design specification. The computing system optimizes an initial segmented design based on the design specification to create an improved segmented design. The computing system perturbs the improved segmented design to create a perturbed segmented design. The computing system optimizes the perturbed segmented design to create a second improved segmented design.