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: Aspects described herein relate to the automatic generation of floorplan layouts based on a floorplan image. Image data comprising an image of an area may be accessed. Based on the image data, a floorplan of the area may be determined. A determination of whether constraints associated with occupancy are met may be made. Based on the constraints being met, and based on the floorplan, a light zones map may be generated. Based on the light zones map, spatial zones corresponding to the light zones may be determined. Based on the spatial zones, candidate floorplan layouts may be generated. Based on application of metaheuristic algorithms or machine-learning models to the candidate floorplan layouts, a subset of candidate floorplan layouts may be selected from the plurality of candidate floorplan layouts. Furthermore, floorplan layout data comprising a subset of floorplan layouts for use by a design application may be generated.

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: 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 and design objectives associated with the urban design project and then generates numerous candidate designs that meet the design criteria and optimize the design objectives to varying degrees. The evaluation engine evaluates each candidate design to generate a set of metrics. The geometry engine modifies the candidate designs based on corresponding metrics to generate candidate designs that better meet the design criteria and more effectively achieve the design objectives.

Abstract: An urban design pipeline is configured to automatically generate design options that meet competing design objectives. A geometry engine within the urban design pipeline generates candidate designs for an urban design project. An evaluation engine within the urban design pipeline evaluates the degree to which each candidate design addresses the competing design objectives to produce a set of design metrics. A tradeoff engine within the urban design pipeline generates a design tradeoff space based on the candidate designs and corresponding design metrics. The tradeoff engine traverses the design tradeoff space based on performance modifications to adjust the degree to which the competing design objectives are addressed. The performance modifications can be obtained from any number of stakeholders in the urban design project.

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: 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: 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: An urban design pipeline is configured to automatically generate design options that meet competing design objectives. A geometry engine within the urban design pipeline generates candidate designs for an urban design project. An evaluation engine within the urban design pipeline evaluates the degree to which each candidate design addresses the competing design objectives to produce a set of design metrics. A tradeoff engine within the urban design pipeline generates a design tradeoff space based on the candidate designs and corresponding design metrics. The tradeoff engine traverses the design tradeoff space based on performance modifications to adjust the degree to which the competing design objectives are addressed. The performance modifications can be obtained from any number of stakeholders in the urban design project.

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: 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.