After a number of solutions have been computationally generated from a problem definition, the Dreamcatcher design explorer presents to the user a set of possible solutions and their associated solution strategies. This user interface provides a sense of the shape of the valid design space and variable interactions. It also assists users in building a mental model of which alternatives are high performing relative to all others in the set. Once the solution has been adequately explored, the designer can modify the problem definition to iteratively generate more relevant solutions.
This is the major differentiator between design optimization tools and Dreamcatcher's exploratory design synthesis process. Arguments for the incorporation of AI into design often default to concerns around replacing the human designer. While many elements that are commonly modeled from scratch such as brackets, adapters and stiffeners may be created more effectively by a system such as Dreamcatcher, complex elements and aspects that are difficult to quantify will require new types of interaction to leverage human intuition and computational rigor in partnership.
Dreamcatcher is pioneering new methods for interactive synthesis and optimization with industry leaders from the automotive, aerospace and manufacturing fields. The Dreamcatcher team consists of the Computational Science and Design Research groups of Autodesk Research in the Office of the CTO with collaborators throughout the larger Autodesk Corporation, industrial partners and academic partners.
To learn more about Autodesk's initiatives in generative design, please visit our generative design information page. Cloud-based Compute and Optimization Infrastructure.
Skip to main content. Project Dreamcatcher What if a CAD system could generate thousands of design options that all meet your specified goals? Tools for Defining Problems Dreamcatcher's problem definition is a format for designers to describe design problems. Diversifying Input Modalities Mimicking the variety of reference material in a typical design brief, in Dreamcatcher the designer explicitly and implicitly documents goals and constraints through a number of input modalities including natural language , image inference and CAD geometry.
Advances in Cloud-Based Computing and Optimization Through a purpose-built, scalable and parallelized cloud computing framework code-named Saturn, Dreamcatcher is able to generate and evaluate solution sets with complexity well beyond that of Generative Design Systems of the past. Design Space Visualization After a number of solutions have been computationally generated from a problem definition, the Dreamcatcher design explorer presents to the user a set of possible solutions and their associated solution strategies. Massimiliano Moruzzi How smart materials will literally reshape the world around us TechCrunch September 17, Details.
University of Toronto Details. Related Projects See All. Design Variations and Optimization.
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