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.

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.