Abstract: An urban design pipeline automatically generates design options for an urban design project. The urban design pipeline includes a geometry engine and an evaluation engine. The geometry engine analyzes design criteria, design objectives, and design heuristics associated with the urban design project and then generates numerous candidate designs. The design criteria specify a property boundary associated with a region of land to be developed. The design objectives indicate a specific type of topology that is derived from an existing urban layout. The design heuristics include different sets of construction rules for generating designs with specific types of topologies. The geometry engine generates candidate designs that conform to the property boundary and have topological characteristics in common with the existing urban layout.

Abstract: A design analysis engine analyzes a CAD assembly to generate a topological model. The topological model is a graph of nodes coupled together via a set of edges. Each node represents a different CAD model in the CAD assembly and each edge represents a different connection between two such CAD models. The design analysis engine also analyzes the CAD assembly to generate a data model corresponding to the topological model. The data model is a structured dataset that includes component entries and connection entries. A component entry includes design data associated with a CAD model in the CAD assembly and a connection entry includes design data associated with a physical or logical connection between two or more such CAD models. A user interacts with the topological model to navigate the CAD assembly, obtain CAD model data, and initiate automatically-performed design tasks.

Abstract: An urban design pipeline automatically generates design options for an urban design project. The urban design pipeline includes a geometry engine and an evaluation engine. The geometry engine analyzes design criteria, design objectives, and design heuristics associated with the urban design project and then generates numerous candidate designs. The design criteria specify a property boundary associated with a region of land to be developed. The design objectives indicate a specific type of topology that is derived from an existing urban layout. The design heuristics include different sets of construction rules for generating designs with specific types of topologies. The geometry engine generates candidate designs that conform to the property boundary and have topological characteristics in common with the existing urban layout.

Abstract: A design application is configured to generate a latent space representation of a fleet of pre-existing vehicles. The design application encodes vehicle designs associated with the fleet of pre-existing vehicles into the latent space representation to generate a first latent space location. The first latent space location represents the characteristic style associated with the fleet of pre-existing vehicles. The design application encodes a sample design provided by a user into the latent space representation to produce a second latent space location. The design application then determines a distance between the first latent space location and the second latent space location. Based on the distance, the design application generates a style metric that indicates the aesthetic similarity between the sample design and the vehicle designs associated with the fleet of pre-existing vehicles. The design application can also generate new vehicle designs based on the latent space representation and the sample design.

Abstract: In various embodiments, an automobile modeling application generates automobile designs. In operation, the automobile modeling application determines a first parameter value associated with a first instance of a first parameterized automobile component. The automobile modeling application then computes a second parameter value associated with a second instance of a second parameterized automobile component based on a structural relationship between the first instance and the second instance. Subsequently, the automobile modeling application generates a computer-aided design (CAD) geometry model for an automobile based on the first parameter value, the second parameter value, and one or more functional relationships defined between two or more instances included in a set of parameterized automobile component instances.

Abstract: A design engine is configured to interact with potential occupants of a structure in order to generate data that defines the usage preferences of those occupants. The design engine generates multiple candidate designs for the structure via a generative design process, and then evaluates each candidate design using a set of metrics determined relative to the usage preferences. Based on these evaluations, the design engine selects at least one candidate design that optimizes the set of metrics across all potential occupants.

Abstract: A design analysis engine analyzes a CAD assembly to generate a topological model. The topological model is a graph of nodes coupled together via a set of edges. Each node represents a different CAD model in the CAD assembly and each edge represents a different connection between two such CAD models. The design analysis engine also analyzes the CAD assembly to generate a data model corresponding to the topological model. The data model is a structured dataset that includes component entries and connection entries. A component entry includes design data associated with a CAD model in the CAD assembly and a connection entry includes design data associated with a physical or logical connection between two or more such CAD models. A user interacts with the topological model to navigate the CAD assembly, obtain CAD model data, and initiate automatically-performed design tasks.

Abstract: Disclosed systems and methods relate to generating and optimizing an architectural design. According to embodiments, a method can include receiving metadata of digital communications and collaboration between members of an organization. The method can also include generating a target network model indicating a work style of the members of the organization; a level of interaction between the members of the organization; a spatial clustering of the members; or a number, a size, and an allocation of rooms with a specialized purpose. The method can further include selecting at least one criterion for the architectural design, where the criterion can include: cost, wellness, community, work style, choice, privacy, or vibrancy. The method can further include determining criteria scores for one or more architectural design prototypes. Moreover, the method can include selecting the architectural design based on the criterion scores.

Abstract: A computer-implemented method for space frame design involves constructing a load stress map in a geometrical boundary representation of a design space, defining attachment points and load application points in the design space, creating a starting network of interconnecting lines between each two of the attachment points and load application points in the design space, assigning load application factors to each line of the starting network of interconnecting lines based on values of the load stress map, generating potential space frame designs by culling different subsets of lines of the starting network of interconnecting lines for each potential space frame design according to variable culling parameters, evaluating the potential space frame designs with respect to optimization parameters, combining the culling parameters for the potential space frame designs the performance score of which is above a predefined performance threshold, and iterating the steps of generating potential space frame designs and e

Abstract: A design application is configured to generate a latent space representation of a fleet of pre-existing vehicles. The design application encodes vehicle designs associated with the fleet of pre-existing vehicles into the latent space representation to generate a first latent space location. The first latent space location represents the characteristic style associated with the fleet of pre-existing vehicles. The design application encodes a sample design provided by a user into the latent space representation to produce a second latent space location. The design application then determines a distance between the first latent space location and the second latent space location. Based on the distance, the design application generates a style metric that indicates the aesthetic similarity between the sample design and the vehicle designs associated with the fleet of pre-existing vehicles. The design application can also generate new vehicle designs based on the latent space representation and the sample design.

Abstract: In various embodiments, a generative design application generates and evaluates automotive designs. In operation, the generative design application computes a first set of metric values based on a set of metrics associated with design goal(s) and a first set of parameter values for a parameterized automobile model. The generative design application then performs optimization operation(s) on the first set of parameter values based on the first set of metric values to generate a second set of parameter values. Subsequently, the generative design application generates at least one design based on the second set of parameter values that is more convergent with respect to at least one of the design goals than a previously generated design. Advantageously, less time and effort are required to generate and evaluate multiple designs and then optimize those designs relative to more manual prior art approaches.

Abstract: A design engine is configured to interact with potential occupants of a structure in order to generate data that defines the usage preferences of those occupants. The design engine generates multiple candidate designs for the structure via a generative design process, and then evaluates each candidate design using a set of metrics determined relative to the usage preferences. Based on these evaluations, the design engine selects at least one candidate design that optimizes the set of metrics across all potential occupants.

Abstract: A design engine for designing an object using structural analysis. The design engine generates a lattice structure for the object comprising a plurality of nodes and a plurality of lines connecting the nodes. The lattice structure is optimized to remove one or more lines using structural analysis based on at least one load-related design requirement. Several design options are provided for generating and optimizing the lattice structure. The design engine then generates a 3D model of the object by thickening each line of the lattice structure into a pipe volume. The thickness of each pipe is determined using structural analysis based on the at least one load-related design requirement. The 3D model represents the volume of the object and is exportable to a fabrication device.

Abstract: In various embodiments, a generative design application generates and evaluates automotive designs. In operation, the generative design application computes a first set of metric values based on a set of metrics associated with design goal(s) and a first set of parameter values for a parameterized automobile model. The generative design application then performs optimization operation(s) on the first set of parameter values based on the first set of metric values to generate a second set of parameter values. Subsequently, the generative design application generates at least one design based on the second set of parameter values that is more convergent with respect to at least one of the design goals than a previously generated design. Advantageously, less time and effort are required to generate and evaluate multiple designs and then optimize those designs relative to more manual prior art approaches.

Abstract: A design application is configured to generate a latent space representation of a fleet of pre-existing vehicles. The design application encodes vehicle designs associated with the fleet of pre-existing vehicles into the latent space representation to generate a first latent space location. The first latent space location represents the characteristic style associated with the fleet of pre-existing vehicles. The design application encodes a sample design provided by a user into the latent space representation to produce a second latent space location. The design application then determines a distance between the first latent space location and the second latent space location. Based on the distance, the design application generates a style metric that indicates the aesthetic similarity between the sample design and the vehicle designs associated with the fleet of pre-existing vehicles. The design application can also generate new vehicle designs based on the latent space representation and the sample design.

Abstract: A design engine is configured to interact with potential occupants of a structure in order to generate data that defines the usage preferences of those occupants. The design engine generates multiple candidate designs for the structure via a generative design process, and then evaluates each candidate design using a set of metrics determined relative to the usage preferences. Based on these evaluations, the design engine selects at least one candidate design that optimizes the set of metrics across all potential occupants.

Abstract: A design engine includes a geometry module and a metric module that interoperate to generate optimal design options. The geometry module initially generates a spectrum of design options for a structure based on project constraints and design criteria set forth by potential occupants of the structure. The metric module then analyzes each design option and generates, for any given design option, a set of metrics that indicates how well the given design option meets the design criteria. The geometry module then generates additional design options in an evolutionary manner to improve the metrics generated for subsequent design options.

Abstract: An urban design pipeline automatically generates design options for an urban design project based on a financial model. The urban design pipeline includes a geometry engine that generates candidate designs to meet various design criteria and design objectives associated with the urban design project. The urban design pipeline also includes an evaluation engine that evaluates the candidate designs based on the financial model. The financial model quantifies the financial performance of any given candidate design. The urban design pipeline operates iteratively to improve the candidate designs until one or more convergence criteria are met, thereby generating a set of design options with acceptable financial performance.

Abstract: A design engine includes a geometry module and a metric module that interoperate to generate optimal design options. The geometry module initially generates a spectrum of design options for a structure based on project constraints and design criteria set forth by potential occupants of the structure. The metric module then analyzes each design option and generates, for any given design option, a set of metrics that indicates how well the given design option meets the design criteria. The geometry module then generates additional design options in an evolutionary manner to improve the metrics generated for subsequent design options.

Abstract: One embodiment of the present invention sets forth a technique for performing tasks associated with a construction project. The technique includes transmitting to a worker, via a mobile computing device worn by the worker, a first instruction related to performing a first task included in a plurality of tasks associated with a construction project, and transmitting to a light-emitting device a command to provide a visual indicator to the worker that facilitates performing the first task, based on an input received from the mobile computing device, determining that the worker has completed the first task of the construction project, selecting, from a database that tracks eligibility of each of the plurality of tasks, a second task included in the plurality of tasks that the worker is eligible to perform, and transmitting to the worker, via the mobile computing device, a second instruction related to performing the second task.