Autodesk, Inc.

Google Inc.

David Benjamin

Danil Nagy

James Stoddart

Lorenzo Villaggi

Damon Lau

Dale Zhao

Rui Wang

Lindsey Wikstrom

Daniel Mario Noviello

Christopher Norman

Bastian Schäfer

Martial Nabankele Somda

Lorenzo Villagi

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.

Techniques for automatically generating urban and neighborhood designs

Techniques are disclosed for designing manufacturing facilities. A design application imports a computer-aided design (CAD) model and metadata associated with a manufacturing facility to generate a data set that specifies geometric and manufacturing constraints of the manufacturing facility. The design application performs optimization operations based on the data set to identify one or more high-performing designs that assign components to docks in the manufacturing facility. The optimization operations can include genetic optimization operations that generate multiple generations of designs, each of which is evaluated based on a travel distance, congestion, and number of turns associated with paths traversed by components for the design.

Genetic optimization techniques for designing manufacturing facilities

Display screen or portion thereof with graphical user interface

A design simulator executes a shape parameter engine that generates geometry corresponding to parking lots. The shape parameter engine implements a library of shapes, action markers, and construction rules to generate designs for parking lots. Each shape is a geometric the that can populate a design surface. Each action marker signifies an action that can occur at a specific location on the design surface. Each construction rule describes how shapes should be placed on the design surface relative to one or more action markers. The shape parameter generator tiles the design surface using the library of shapes, action markers, and construction rules to generate valid parking lot designs.

Generating designs for multi-family housing projects using shape parameter transformations

Techniques for accelerating the designing of manufacturing facilities

A design engine is configured to automatically generate and position design elements within designs for a multi-family housing project that corresponds to a parcel of land. Each design simultaneously meets local construction regulations while also meeting specific financial targets. The design engine generates a site model that reflects geometric attributes of the parcel of land, a building model that describes one or more buildings configured to occupy the parcel of land, and a parking lot model that describes a parking lot configured to occupy a remaining portion of the parcel of land. A design simulator executes various types of physical simulations with the site model, the building model, and/or the parking lot model to generate a vast number of different design options having widely varying configurations of design elements. The design engine evaluates these design options to identify those which balance compliance with construction regulations and achievement of financial targets.

Generating designs for multi-family housing projects using physical simulations of design elements

One or more embodiments disclose techniques for generating a layout for a physical space or a building. The techniques include generating a layout based on a floorplan of a physical space, generating a plurality of spatial unit grids corresponding to a plurality of spatial units to be placed in the physical space, identifying a placement for a first spatial unit grid in the plurality of spatial unit grids within the layout by matching a corner cell in the first spatial unit grid with a given available cell in the layout, generating a score associated with the placement for the first spatial unit grid based on one or more placement parameters that define placement constraints for a first spatial unit included in the plurality of spatial units and corresponding to the first spatial unit grid, and placing the first spatial unit grid in the layout based on the score.

Computer-implemented techniques for creating layouts for physical spaces

A voxel-based design approach enables the creating and modifying of a design model comprising a 3D grid of discrete voxels that is represented by a voxel data structure. The voxel data structure comprises voxel-level entries, each entry corresponding to a voxel based on the 3D location within the 3D grid. The voxel data structure includes a design-level entry for storing design-level performance metrics. The system updates the voxel data structure to reflect user modifications to the design model and renders a visualization of the updated design model. The system displays a per-voxel heat map for the design model for a selected performance metric based on the voxel data structure. The design system displays multiple optimized design solutions based on corresponding optimized voxel data structures. The system generates the multiple optimized design solutions based on a voxel-based optimization technique. The system also performs a voxel-based recommendation visualization technique.

Voxel-based approach for design models

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.

Generative design techniques for automobile designs

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.

Generative design pipeline for urban and neighborhood planning

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. In this manner, the urban design pipeline generates and/or modifies candidate designs to generate design options that balance competing design objectives that potentially originate from multiple stakeholders.

Techniques for automatically analyzing competing design objectives when generating designs for urban design projects

A design engine is configured to automatically generate designs for multi-family housing projects that simultaneously meet local construction regulations while also meeting specific financial targets. The design engine includes a design analyzer that generates design trends based on a database of historical designs. A feature extractor within the design analyzer performs a geometrical analysis of the historical designs to generate a library of features. A feature parameterizer within the design analyzer performs a parameterization process to generate one or more parametric values for each feature. A parameter correlator within the design analyzer compares different subsets of parametric values to identify relationships between those values. A trend aggregator identifies relationships that occur across multiple historical designs to determine a set of design trends. The design engine generates design options using the design trends. Design options generated in this manner include design features that historically promote both feasibility and desirability.

Generating designs for multi-family housing projects using historical design trends

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.

Techniques for automatically generating designs having characteristic topologies for urban design projects

A computer-implemented method for generating a building design for a building site having a complex shape comprises: generating a first group of processing areas from a plurality of processing areas associated with the building design and a second group of processing areas from the plurality of processing areas; generating a first building footprint leg that is sized to include the first group of processing areas and a second building footprint leg that is sized to include the second group of processing areas; at a pivot location, joining the first building footprint leg and the second building footprint leg at an aperture angle; performing a numerical simulation to position the first group of processing areas in the first building footprint leg and the second group of processing areas in the second building footprint leg to generate a candidate building design for the building site; and rendering the candidate building design.

Generating viable building designs for complex sites

A workflow engine processes a work problem to generate solutions for the work problem comprising a plurality of related optimization problems. The work problem may comprise a second optimization problem that is dependent on a first optimization problem, such that at least one solution for the first optimization problem is to be utilized as an initial solution for the second optimization problem. The workflow engine generates and stores a branch object for each optimization problem, each branch object specifying a solver engine assigned for processing the optimization problem and dependency information indicating a dependency relationship between the optimization problem and another optimization problem. The workflow engine processes the work problem based on the branch objects by initiating each solver engine to perform optimization operations on the assigned optimization problem based on the corresponding branch object to generate one or more solutions for the assigned optimization problem.

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New York, NY, United States of America

David Benjamin