Techniques for analyzing vehicle design deviations using deep learning with neural networks

    公开(公告)号:US11842262B2

    公开(公告)日:2023-12-12

    申请号:US18045809

    申请日:2022-10-11

    Applicant: AUTODESK, INC.

    CPC classification number: G06N3/045 G06F17/15 G06F30/15 G06N3/08

    Abstract: A design application is configured to generate a latent space representation of a fleet of pre-existing vehicles. The design application encodes vehicle designs associated with the fleet of pre-existing vehicles into the latent space representation to generate a first latent space location. The first latent space location represents the characteristic style associated with the fleet of pre-existing vehicles. The design application encodes a sample design provided by a user into the latent space representation to produce a second latent space location. The design application then determines a distance between the first latent space location and the second latent space location. Based on the distance, the design application generates a style metric that indicates the aesthetic similarity between the sample design and the vehicle designs associated with the fleet of pre-existing vehicles. The design application can also generate new vehicle designs based on the latent space representation and the sample design.

    Techniques for generating comprehensive information models for automobile designs

    公开(公告)号:US11775709B2

    公开(公告)日:2023-10-03

    申请号:US16387526

    申请日:2019-04-17

    Applicant: AUTODESK, INC.

    CPC classification number: G06F30/23 G06F30/15 G06T19/00 G06T2210/36

    Abstract: In various embodiments, an automobile modeling application generates automobile designs. In operation, the automobile modeling application determines a first parameter value associated with a first instance of a first parameterized automobile component. The automobile modeling application then computes a second parameter value associated with a second instance of a second parameterized automobile component based on a structural relationship between the first instance and the second instance. Subsequently, the automobile modeling application generates a computer-aided design (CAD) geometry model for an automobile based on the first parameter value, the second parameter value, and one or more functional relationships defined between two or more instances included in a set of parameterized automobile component instances. Advantageously, because the automobile modeling application automatically propagates changes throughout the automobile design, the amount of time required to make significant changes to the automobile design can be substantially reduced.

    Automated generation and evaluation of architectural designs

    公开(公告)号:US11748527B2

    公开(公告)日:2023-09-05

    申请号:US17820867

    申请日:2022-08-18

    Applicant: AUTODESK, INC.

    CPC classification number: G06F30/13

    Abstract: A design engine is configured to interact with potential occupants of a structure in order to generate data that defines the usage preferences of those occupants. The design engine generates multiple candidate designs for the structure via a generative design process, and then evaluates each candidate design using a set of metrics determined relative to the usage preferences. Based on these evaluations, the design engine selects at least one candidate design that optimizes the set of metrics across all potential occupants.

    Generative design techniques for automobile designs

    公开(公告)号:US11475178B2

    公开(公告)日:2022-10-18

    申请号:US16387527

    申请日:2019-04-17

    Applicant: AUTODESK, INC.

    Abstract: In various embodiments, a generative design application generates and evaluates automotive designs. In operation, the generative design application computes a first set of metric values based on a set of metrics associated with design goal(s) and a first set of parameter values for a parameterized automobile model. The generative design application then performs optimization operation(s) on the first set of parameter values based on the first set of metric values to generate a second set of parameter values. Subsequently, the generative design application generates at least one design based on the second set of parameter values that is more convergent with respect to at least one of the design goals than a previously generated design. Advantageously, less time and effort are required to generate and evaluate multiple designs and then optimize those designs relative to more manual prior art approaches.

    Automated generation and evaluation of architectural designs

    公开(公告)号:US11423191B2

    公开(公告)日:2022-08-23

    申请号:US16112562

    申请日:2018-08-24

    Applicant: AUTODESK, INC.

    Abstract: A design engine is configured to interact with potential occupants of a structure in order to generate data that defines the usage preferences of those occupants. The design engine generates multiple candidate designs for the structure via a generative design process, and then evaluates each candidate design using a set of metrics determined relative to the usage preferences. Based on these evaluations, the design engine selects at least one candidate design that optimizes the set of metrics across all potential occupants.

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