Abstract: In various embodiments, a parameter domain graph application generates UV-net representations of 3D CAD objects for machine learning models. In operation, the parameter domain graph application generates a graph based on a B-rep of a 3D CAD object. The parameter domain graph application discretizes a parameter domain of a parametric surface associated with the B-rep into a 2D grid. The parameter domain graph application computes at least one feature at a grid point included in the 2D grid based on the parametric surface to generate a 2D UV-grid. Based on the graph and the 2D UV-grid, the parameter domain graph application generates a UV-net representation of the 3D CAD object. Advantageously, generating UV-net representations of 3D CAD objects that are represented using B-reps enables the 3D CAD objects to be processed efficiently using neural networks.

Abstract: Techniques for generative design include a computer-implemented method for solving a design problem comprising initializing values for one or more categorical and continuous design variables, and performing a design iteration by generating sample vectors for each of one or more categorical design variables based on the categorical design variable probabilities, solving one or more governing equations for the design problem based on values of the continuous design variables and the sample vectors, computing a value of one or more constraint functions and an objective function, computing first gradients of the objective function and the constraint functions with respect to each of the continuous design variables, computing second gradients of the objective function and the constraint functions with respect to the categorical design variable probabilities, and updating values for the continuous design variables based on the first gradients and values for the categorical design variable probabilities based on the

Abstract: Techniques for generative design include a computer-implemented method for solving a design problem comprising initializing values for one or more categorical design variable probabilities and one or more continuous design variables, and performing a design iteration by performing one or more iterations to update the categorial design variable probabilities by generating sample vectors for each of one or more categorical design variables based on the categorical design variable probabilities, computing first gradients of an objective function and one or more constraint functions with respect to the categorical design variable probabilities, and updating values for the categorical design variable probabilities based on the first gradients, then updating the sample vectors based on the updated categorical design variable probability values, computing second gradients of the objective and constraint functions with respect to each of the continuous design variables, and updating values for the continuous design v

Abstract: Techniques for interactive generative design with sensitivity analysis and probability visualization for categorical design variables include a computer-implemented method for evaluating an impact of categorical design variables on a design problem solution comprises receiving information regarding choices for one or more categorical design variables associated with each of a plurality of design members of a design problem, determining a respective sensitivity of an objective function to the choices for the one or more categorical design variables for each design member of the plurality of design members, determining a respective visual aspect for each design member based on the respective sensitivity, displaying, on a user interface, a graphical depiction of the plurality of design members, wherein each design member is displayed using the respective visual aspect, and displaying, on the user interface, a key for interpreting the respective visual aspects.

Abstract: One embodiment of the present invention sets forth a technique for performing 3D shape generation. This technique includes generating semantic features associated with an input sketch. The technique also includes generating, using a generative machine learning model, a plurality of predicted shape embeddings from a set of fully masked shape embeddings based on the semantic features associated with the input sketch. The technique further includes converting the predicted shape embeddings into one or more 3D shapes. The input sketch may be a casual doodle, a professional illustration, or a 2D CAD software rendering. Each of the one or more 3D shapes may be a voxel representation, an implicit representation, or a 3D CAD software representation.

Abstract: One embodiment of the present invention sets forth a technique for generating 3D CAD model representations of three-dimensional objects. The technique includes generating a vertex list that includes a first ordered list of elements representing vertex coordinates and sampling a first index from the vertex list based on a first probability distribution. The technique also includes generating an edge list and sampling a second index from one or more indices into the edge list. The technique further includes generating an element in a face list, dereferencing the element in the face list to retrieve an element in the edge list, and dereferencing an element in the edge list to retrieve a vertex coordinate from an element in the vertex list. The technique further includes generating an indexed boundary representation for the 3D CAD model based on at least the vertex list, the edge list, and the face list.

Abstract: One embodiment of the present invention sets forth a technique for generating 3D CAD model representations of three-dimensional objects in boundary representation format. The technique includes generating an indexed boundary representation of the generated 3D CAD model. The indexed boundary representation includes ordered lists of vertices, edges, and faces defining the generated 3D CAD model, where the edges are encoded as references to vertices in the vertex list and the face are encoded as references to edges in the edge list. The technique further includes converting the indexed boundary representation of the generated 3D CAD model into a boundary representation of the 3D CAD model through the application of heuristic algorithms to the indexed boundary representation. The technique is optionally guided by conditional data associated with the 3D CAD model to be generated, including a 2D image, a 3D collection of volume elements, or a 3D point cloud.

Abstract: Methods, systems, and apparatus, including medium-encoded computer program products include: obtaining a design space for a modeled object, for which a corresponding physical structure is to be manufactured, and one or more design criteria for the modeled object; iteratively modifying a first three-dimensional shape of the modeled object in the design space in accordance with the one or more design criteria, the iteratively modifying includes forming a second three-dimensional shape of the modeled object based on the first three-dimensional shape of the modeled object, where the second three-dimensional shape conforms to a predefined shape-type requirement, and penalizing modifications of the first three-dimensional shape that deviate from the second three-dimensional shape; and providing the first or second three dimensional shape of the modeled object for use in manufacturing the physical structure using one or more computer-controlled manufacturing systems.

Abstract: A generative design system includes a solver and a modeling tool comprising a visual programming interface and a design workflow script. The visual programming interface enables the user to specify a design problem including design constraints comprising parameters associated with standard building components, such as beams and joints. After the design problem is specified by the user, the modeling tool executes the design workflow script to automatically perform a design workflow that generates a design solution for the design problem. The design workflow script controls the operations of the modeling tool and the solver to interact in a collaborative manner to execute the design workflow comprising an ordered sequence of operations. The design solution comprises a 3D model of a modular beam structure that can be easily fabricated using standard building components, such as standardized beams and joints.

Abstract: In various embodiments, a training application trains machine learning models to perform tasks associated with 3D CAD objects that are represented using B-reps. In operation, the training application computes a preliminary result via a machine learning model based on a representation of a 3D CAD object that includes a graph and multiple 2D UV-grids. Based on the preliminary result, the training application performs one or more operations to determine that the machine learning model has not been trained to perform a first task. The training application updates at least one parameter of a graph neural network included in the machine learning model based on the preliminary result to generate a modified machine learning model. The training application performs one or more operations to determine that the modified machine learning model has been trained to perform the first task.

Abstract: In various embodiments, a parameter domain graph application generates UV-net representations of 3D CAD objects for machine learning models. In operation, the parameter domain graph application generates a graph based on a B-rep of a 3D CAD object. The parameter domain graph application discretizes a parameter domain of a parametric surface associated with the B-rep into a 2D grid. The parameter domain graph application computes at least one feature at a grid point included in the 2D grid based on the parametric surface to generate a 2D UV-grid. Based on the graph and the 2D UV-grid, the parameter domain graph application generates a UV-net representation of the 3D CAD object. Advantageously, generating UV-net representations of 3D CAD objects that are represented using B-reps enables the 3D CAD objects to be processed efficiently using neural networks.

Abstract: In various embodiments, an inference application performs tasks associated with 3D CAD objects that are represented using B-reps. A UV-net representation of a 3D CAD object that is represented using a B-rep includes a set of 2D UV-grids and a graph. In operation, the inference application maps the set of 2D UV-grids to a set of node feature vectors via a trained neural network. Based on the node feature vectors and the graph, the inference application computes a final result via a trained graph neural network. Advantageously, the UV-net representation of the 3D CAD object enabled the trained neural network and the trained graph neural network to efficiently process the 3D CAD object.

Abstract: In various embodiments, a style comparison application generates visualization(s) of geometric style gradient(s). The style comparison application generates a first set of style signals based on a first 3D CAD object and generates a second set of style signals based on a second 3D CAD object. Based on the first and second sets of style signals, the style comparison application computes a different partial derivative of a style comparison metric for each position included in a set of positions associated with the first 3D CAD object to generate a geometric style gradient. The style comparison application generates a graphical element based on at least one of the direction or the magnitude of a vector in the geometric style gradient and positions the graphical element relative to a graphical representation of the first 3D CAD object within a graphical user interface to generate a visualization of the geometric style gradient.

Abstract: In various embodiments, a style comparison application compares geometric styles of different three dimensional (3D) computer-aided design (CAD) objects. In operation, the style comparison application executes a trained neural network one or more times to map 3D CAD objects to feature map sets. The style comparison application computes a first set of style signals based on a first feature set included in the feature map sets. The style comparison application computes a second set of style signals based on a second feature set included in the feature map sets. Based on the first set of style signals and the second set of style signals, the style comparison application determines a value for a style comparison metric. The value for the style comparison metric quantifies a similarity or a dissimilarity in geometric style between a first 3D CAD object and a second 3D CAD object.

Abstract: In various embodiments, a style comparison metric application generates a style comparison metric for pairs of different three dimensional (3D) computer-aided design (CAD) objects. In operation, the style comparison metric application executes a trained neural network any number of times to map 3D CAD objects to feature maps. Based on the feature maps, the style comparison metric application computes style signals. The style comparison metric application determines values for weights based on the style signals. The style comparison metric application generates the style comparison metric based on the weights and a parameterized style comparison metric.