Ansys, Inc.

Jay Prakash Pathak

Rishikesh Ranade

Norman H Chang

Jimin Wen

Akhilesh Kumar

Wen-Tze Chuang

Lei Yin

Xi Chen

Ying Shiun Li

Ming-Chih Shih

En Cih Yang

Bolong Zhang

Derek Christopher Hill

Jogesh Chopra

A generative machine learning model, such as a convolutional neural network (CNN), can be trained with solutions from a topology optimization solver for a solution for a topology of a set of structures so that the generative machine learning model can generate a plurality of alternative designs for a structure that are alternative topology optimizations (for the structure) for a set of initial setup parameters. The generative model when being trained includes a generative network and a discriminator network. The generative model can be trained using outputs from a CNN autoencoder for densities and a CNN autoencoder for strain energies.

Method and system for generative design based on deep learning and topology optimization

A specification for a semi-conductor chip is received. The specification specifies a set of photomasks associated with a metal layer of the semi-conductor chip. Multiple portions of an area of the metal layer are identified. A respective image is generated for each portion of the area based on the photomasks. A respective drawn density of metal wires for each portion of the area is calculated. A trained machine learning model is invoked to predict a respective silicon density of metal wires for each respective portion of the area based on an image and a drawn density for the respective portion of the area. A silicon density for the area of the metal layer is calculated based on a combination of predicted silicon densities for the multiple portions of the area. The combination is based on an average value of the predicted silicon densities for the multiple portions of the area.

Methods and systems for predicting silicon density for a metal layer of semi-conductor chip via machine learning

Partial differential equations used to simulate physical systems can be solved, in one embodiment, by a solver that has been trained with a set of generative neural networks that operated at different resolutions in a solution space of a domain that defines the physical space of the physical system. The solver can operate in a latent vector space which encodes solutions to the PDE in latent vectors in the latent vector space. The variables of the PDE can be partially decoupled in the latent vector space while the solver operates. The domain can be divided into subdomains that are classified based on their positions in the domain.

Machine-learning based solver of coupled-partial differential equations

A method and apparatus for determining spatial characteristics of three-dimensional objects is described. In an exemplary embodiment, the device receives a point cloud representation of a three-dimensional surface structure of a plurality of objects. The device may further generate a set of bins to represent the three-dimensional surface structure based on the point cloud representation, each bin corresponding to a spatial occupancy related to the point cloud representation, each bin including a respective type indicating a spatial relationship of the surface structures and a corresponding spatial occupancy of the bin. In addition, the device may encode the set of bins using a convolutional neural network. The device may further determine a classification for the spatial characteristic of the surface structures based on the convolutional neural network with the encoded set of bins.

Representation learning using machine learning classification tasks based on point clouds of interacting 3D surfaces

Systems and methods are provided for determining a confidence level on contact pairs identified through traditional contact detection algorithms for an assembly of bodies. A plurality of testing tolerances are identified. For each of the identified testing tolerances, a determination is made as to whether a distance between any two faces of the bodies in the assembly is less than that identified testing tolerance and when this happens a count of positive detections is increased. A ratio of the count of positive detections to the total number of times the detections are run tolerances is determined (called repeatability of a face pair), where the confidence level is based on the determined repeatability ratio.

Reporting statistical confidence in detecting contacts between bodies in a simulation

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Pleasanton, CA, United States of America

Jay Prakash Pathak