公开(公告)号：US20240220532A1

公开(公告)日：2024-07-04

申请号：US18150129

申请日：2023-01-04

Applicant: AUTODESK, INC.

Inventor： Andre Maurice SCHREIBER ,  Ran ZHANG

IPC: G06F16/532 ,  G06F16/54

CPC classification number: G06F16/532 ,  G06F16/54

Abstract: One embodiment of the present invention sets forth a technique for analyzing similarities associated with a plurality of shapes. The technique includes determining a first embedding for a first query shape associated with a first format and a first plurality of embeddings for a first plurality of shapes associated with a second format, wherein the first embedding and the first plurality of embeddings are generated by one or more trained machine learning models based on the first query shape and the first plurality of shapes. The technique also includes matching, based on the first embedding and the first plurality of embeddings, the first query shape to one or more shapes included in the first plurality of shapes. The technique further includes outputting the one or more shapes in a first response associated with the first query shape.

公开(公告)号：US20220130110A1

公开(公告)日：2022-04-28

申请号：US17083147

申请日：2020-10-28

Applicant: AUTODESK, INC.

Inventor： Ran ZHANG ,  Morgan FABIAN ,  Ebot NDIP-AGBOR ,  Lee Morris TAYLOR

IPC: G06T17/20 ,  G06N20/00 ,  G06N3/08

Abstract: In various embodiments, a topology optimization application solves a topology optimization problem associated with designing a three-dimensional (“3D”) object. The topology optimization application coverts a first shape having a first resolution and representing the 3D object to a coarse shape having a second resolution that is lower than the first resolution. Subsequently, the topology optimization application computes coarse structural analysis data based on the coarse shape. The topology optimization application then uses a trained machine learning model to generate a second shape having the first resolution and representing the 3D object based on the first shape and the coarse structural analysis data. The trained machine learning model modifies a portion of a shape having the first resolution based on structural analysis data having the second resolution. Advantageously, generating the second shape based on structural analysis data having a lower resolution reduces computational complexity relative to prior art techniques.

公开(公告)号：US20240220856A1

公开(公告)日：2024-07-04

申请号：US18150135

申请日：2023-01-04

Applicant: AUTODESK, INC.

Inventor： Andre Maurice SCHREIBER ,  Ran ZHANG

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: One embodiment of the present invention sets forth a technique for training machine learning models to generate embeddings for different shapes. The technique includes executing two or more machine learning models to generate embeddings from shapes associated with multiple formats. The technique also includes computing a first plurality of similarities between positive pairs of embeddings that include two different embeddings for the same shape, and computing a second plurality of similarities between negative pairs of embeddings that include embeddings for different shapes. The technique further includes training the machine learning models based on the computed similarities.

公开(公告)号：US20230343058A1

公开(公告)日：2023-10-26

申请号：US18346754

申请日：2023-07-03

Applicant: AUTODESK, INC.

Inventor： Ran ZHANG ,  Morgan FABIAN ,  Ebot Etchu NDIP-AGBOR ,  Lee Morris TAYLOR

IPC: G06T19/20 ,  G06N3/08 ,  G06T3/40 ,  G06T17/20

CPC classification number: G06T19/20 ,  G06N3/08 ,  G06T3/40 ,  G06T17/205 ,  G06T2219/2016 ,  G06T2219/2021

Abstract: In various embodiments, a training application trains a machine learning model to modify portions of shapes when designing 3D objects. The training application converts first structural analysis data having a first resolution to first coarse structural analysis data having a second resolution that is lower than the first resolution. Subsequently, the training application generates one or more training sets based on a first shape, the first coarse structural analysis data, and a second shape that is derived from the first shape. Each training set is associated with a different portion of the first shape. The training application then performs one or more machine learning operations on the machine learning model using the training set(s) to generate a trained machine learning model. The trained machine learning model modifies at least a portion of a shape having the first resolution based on coarse structural analysis data having the second resolution.

公开(公告)号：US20220318947A1

公开(公告)日：2022-10-06

申请号：US17374722

申请日：2021-07-13

Applicant: AUTODESK, INC.

Inventor： Thomas Ryan DAVIES ,  Alexander Ray CARLSON ,  Aditya SANGHI ,  Tarkeshwar Kumar SHAH ,  Divya SIVASANKARAN ,  Anup Bhalchandra WALVEKAR ,  Ran ZHANG

IPC: G06T3/40

Abstract: One embodiment of the present invention sets forth a technique for adding dimensions to a target drawing. The technique includes generating a first set of node embeddings for a first set of nodes included in a target graph that represents the target drawing. The technique also includes receiving a second set of node embeddings for a second set of nodes included in a source graph that represents a source drawing, where one or more nodes included in the second set of nodes are associated with one or more source dimensions included in the source drawing. The technique further includes generating a set of mappings between the first and second sets of nodes based similarities between the first set of node embeddings and the second set of node embeddings, and automatically placing the one or more source dimensions within the target drawing based on the set of mappings.

公开(公告)号：US20220130127A1

公开(公告)日：2022-04-28

申请号：US17083153

申请日：2020-10-28

Applicant: AUTODESK, INC.

Inventor： Ran ZHANG ,  Morgan FABIAN ,  Ebot NDIP-AGBOR ,  Lee Morris TAYLOR

IPC: G06T19/20 ,  G06N3/08 ,  G06T3/40 ,  G06T17/20

Abstract: In various embodiments, a training application trains a machine learning model to modify portions of shapes when designing 3D objects. The training application converts first structural analysis data having a first resolution to first coarse structural analysis data having a second resolution that is lower than the first resolution. Subsequently, the training application generates one or more training sets based on a first shape, the first coarse structural analysis data, and a second shape that is derived from the first shape. Each training set is associated with a different portion of the first shape. The training application then performs one or more machine learning operations on the machine learning model using the training set(s) to generate a trained machine learning model. The trained machine learning model modifies at least a portion of a shape having the first resolution based on coarse structural analysis data having the second resolution.