Invention Grant
- Patent Title: Machine learning techniques for generating designs for three-dimensional objects
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Application No.: US17083147Application Date: 2020-10-28
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Publication No.: US11468634B2Publication Date: 2022-10-11
- Inventor: Ran Zhang , Morgan Fabian , Ebot Ndip-Agbor , Lee Morris Taylor
- Applicant: AUTODESK, INC.
- Applicant Address: US CA San Rafael
- Assignee: AUTODESK, INC.
- Current Assignee: AUTODESK, INC.
- Current Assignee Address: US CA San Rafael
- Agency: Artegis Law Group, LLP
- Main IPC: G06T17/20
- 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.
Public/Granted literature
- US20220130110A1 MACHINE LEARNING TECHNIQUES FOR GENERATING DESIGNS FOR THREE-DIMENSIONAL OBJECTS Public/Granted day:2022-04-28
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