SHAPED-BASED TECHNIQUES FOR EXPLORING DESIGN SPACES

• US 20200133449A1
• Filed: 10/29/2018
• Published: 04/30/2020
• Est. Priority Date: 10/29/2018
• Status: Active Grant

• Current Assignee: Autodesk, Inc.
• Original Assignee: Autodesk, Inc.
• Inventors: DAVIES, Thomas, HALEY, Michael, DANIELYAN, Ara, FABIAN, Morgan
• Granted Patent: US 11,126,330 B2
• Time in Patent Office: Days
• CPC Class Codes: G06F 18/23 Clustering techniques
• CPC Class Codes: G06F 3/048 Interaction techniques based on graphical user interfaces [GUI]
• CPC Class Codes: G06F 3/0482 Interaction with lists of selectable items, e.g. menus
• CPC Class Codes: G06F 3/0484 for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
• CPC Class Codes: G06F 3/04845 for image manipulation, e.g. dragging, rotation, expansion or change of colour
• CPC Class Codes: G06F 30/00 Computer-aided design [CAD]
• CPC Class Codes: G06F 30/10 Geometric CAD
• CPC Class Codes: G06F 30/27 using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
• CPC Class Codes: G06N 3/04 Architecture, e.g. interconnection topology
• CPC Class Codes: G06N 3/045 Combinations of networks
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G06N 3/088 Non-supervised learning, e.g. competitive learning
• CPC Class Codes: G06T 11/206 Drawing of charts or graphs
• CPC Class Codes: G06T 15/20 Perspective computation
• CPC Class Codes: G06T 2200/24 involving graphical user interfaces [GUIs]

Abstract

In various embodiments, a training application generates a trained encoder that automatically generates shape embeddings having a first size and representing three-dimensional (3D) geometry shapes, First, the training application generates a different view activation for each of multiple views associated with a first 3D geometry based on a first convolutional neural network (CNN) block. The training application then aggregates the view activations to generate a tiled activation. Subsequently, the training application generates a first shape embedding having the first size based on the tiled activation and a second CNN block. The training application then generates multiple re-constructed views based on the first shape embedding. The training application performs training operation(s) on at least one of the first CNN block and the second CNN block based on the views and the re-constructed views to generate the trained encoder.

Claims

1. A computer-implemented method for automatically exploring design spaces, the method comprising:

displaying one or more designs included in a first plurality of designs via a graphical user interface (GUI), wherein the first plurality of designs defines, at least in part, a design space, and each design included in the first plurality of designs is associated with a different shape embedding that describes the shape of the design;

in response to receiving a command via the GUI, determining one or more target designs included in the first plurality of designs based on at least a first shape embedding and first metadata associated with at least a first design included in the first plurality of designs; and

displaying the one or more target designs via the GUI, wherein the target designs reflect a preference associated with the first command.

2. The method of claim 1, wherein the first metadata comprises at least one of functional data, manufacturability data;

manufacturing status data, a constraint associated with a generative design flow, and a series of intermediate outcomes associated with the generative design flow.

3. The method of claim 1, wherein determining the one or more target designs comprises:

determining a timeline that is associated with a generative design flow based on the first metadata;

determining a plurality of intermediate outcomes included in the first plurality of designs based on the timeline; and

performing one or more clustering operations on the plurality of intermediate outcomes based on the shape embeddings to generate a first shape cluster that includes the one or more target designs.

4. The method of claim 1, wherein displaying the one or more target designs comprises generating an annotated timeline based on the one or more target designs, and displaying the annotated timeline via the GUI.

5. The method of claim 1, wherein determining the one or more target designs comprises:

performing one or more clustering operations on the first plurality of designs based on the shape embeddings to generate a plurality of shape clusters;

generating a plurality of positions based on the first plurality of designs and the first metadata, wherein each position included in the plurality of positions is associated with a different design included in the first plurality of designs;

for each position included in the plurality of positions, determining a color associated with the position based on the plurality of shape clusters to generate a colored position included in a plurality of colored positions; and

generating a plot that includes the one or more target designs based on the plurality of colored positions.

6. The method of claim 1, further comprising:

performing one or more clustering operations on the first plurality of designs based on the shape embeddings to generate a plurality of shape clusters; and

performing at least one data analysis operation based on the plurality of shape clusters and the first metadata to determine the one or more target designs

7. The method of claim 1, wherein determining the one or more target designs comprises generating a scatter plot based on the shape embeddings and the first metadata.

8. The method of claim 1, wherein the design space is associated with a generative design flow.

9. The method of claim 1, further comprising:

for each design included in the first plurality of designs, computing a different set of views based on a three-dimensional (3D) geometry associated with the design; and

for each set of views;

computing a different shape embedding based on the set of views and a trained encoder that includes at least one convolutional neural network (CNN).

10. One or more non-transitory computer readable media including instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of:

displaying one or more designs included in a first plurality of designs via a graphical user interface (GUI), wherein the first plurality of designs defines, at least in part, a design space; and

each design included in the first plurality of designs is associated with a different shape embedding that describes the shape of the design;

in response to receiving a command via the GUI, determining one or more target designs included in the first plurality of designs based on at least a first shape embedding and first metadata associated with at least a first design included in the first plurality of designs; and

displaying the one or more target designs via the GUI, wherein the target designs reflect a preference associated with the first command.

11. The one or more non-transitory computer readable media of claim 10, wherein the first metadata comprises at least one of a property of the first design;

a property of the design space; and

a property of a design flow associated with the design space.

12. The one or more non-transitory computer readable media of claim 10, wherein determining the one or more target designs comprises:

determining a timeline that is associated with a generative design flow based on the first metadata;

determining a plurality of intermediate outcomes included in the first plurality of designs based on the timeline; and

performing one or more comparison operations on the shape embeddings to determine the one or more target designs from the plurality of intermediate outcomes.

13. The one or more non-transitory computer readable media of claim 10, wherein displaying the one or more target designs comprises generating an annotated timeline based on the one or more target designs, and displaying the annotated timeline via the GUI.

14. The one or more non-transitory computer readable media of claim 10, wherein determining the one or more target designs comprises:

performing one or more clustering operations on the first plurality of designs based on the shape embeddings to generate a plurality of shape clusters;

generating a plurality of positions based on the first plurality of designs and the first metadata, wherein each position included in the plurality of positions is associated with a different design included in the first plurality of designs;

for each position included in the plurality of positions, determining a symbol associated with the position based on the plurality of shape clusters to generate an annotated position included in a plurality of annotated positions; and

generating a plot that includes the one or more target designs based on the plurality of annotated positions.

15. The one or more non-transitory computer readable media of claim 10, further comprising:

performing one or more clustering operations on the first plurality of designs based on the shape embeddings to generate a plurality of shape clusters; and

performing at least one data analysis operation based on the plurality of shape clusters and the first metadata to determine the one or more target designs

16. The one or more non-transitory computer readable media of claim 10, wherein determining the one or more target designs comprises generating a scatter plot based on the shape embeddings and the first metadata.

17. The one or more non-transitory computer readable media of claim 10, wherein the one or more target designs include one or more intermediate outcomes, and further comprising promoting the one or more intermediate outcomes to one or more final outcomes.

18. The one or more non-transitory computer readable media of claim 10, further comprising, for each design included in the first plurality of designs, computing a different shape embedding based on the design and a trained encoder that includes at least one convolutional neural network (CNN).

19. A system, comprising:

one or more memories storing instructions; and

one or more processors that are coupled to the one or more memories and, when executing the instructions, are configured to;

display one or more designs included in a first plurality of designs via a graphical user interface (GUI), wherein the first plurality of designs defines, at least in part, a design space, and each design included in the first plurality of designs is associated with a different shape embedding that describes the shape of the design;

in response to receiving a command via the GUI, determine one or more target designs included in the first plurality of designs based on at least a first shape embedding and first metadata associated with at least a first design included in the first plurality of designs; and

Display the one or more target designs via the GUI, wherein the target designs reflect a preference associated with the first command.

20. The system of claim 19, wherein the one or ore processors are further configured to:

for each design included in the first plurality of designs, compute a different set of views based on a three-dimensional (3D) geometry associated with the design; and

for each set of views, compute a different shape embedding based on the set of views and an encoder portion of a trained multi-view autoencoder.