The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Oct. 11, 2022

Filed:

Oct. 28, 2020
Applicant:

Autodesk, Inc., San Rafael, CA (US);

Inventors:

Ran Zhang, San Jose, CA (US);

Morgan Fabian, San Francisco, CA (US);

Ebot Ndip-Agbor, San Rafael, CA (US);

Lee Morris Taylor, Cedar Crest, NM (US);

Assignee:

AUTODESK, INC., San Francisco, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 17/20 (2006.01); G06N 20/00 (2019.01); G06N 3/08 (2006.01);
U.S. Cl.
CPC ...
G06T 17/20 (2013.01); G06N 3/08 (2013.01); G06N 20/00 (2019.01);
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.


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