A validation neural network (VNN) metamodel for predicting the performance of deep generative designs

James Cunningham, Conrad S. Tucker

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

This work presents a deep neural network method for approximating the performance of generated design concepts. This deep learning meta-modeling approach minimizes the need for costly simulations that test for design concept feasibility by discovering the visual features of a design that correlated to good and bad performance. These form-function relationships are discovered by simply observing the pixels of images of many candidate designs and their corresponding performance in a simulation environment. As opposed to existing metamodeling techniques, this evaluation is agnostic to the simulation environment and applicable to any design space in which form and function are closely linked. A case study is presented in which 2D sketches of boats generated from a deep generative model are evaluated in a simulation environment based on their ability to travel through water without sinking as well as their speed of travel. It is shown through simulation that 57.5% of the designs, which are validated according to their form during the generation process, fail in their intended function. Additionally, the trained VNN is able to classify designs it has never seen before as successful or failing with an accuracy of 86.6% and an F1-Score of 0.806.

Original languageEnglish (US)
Title of host publication44th Design Automation Conference
PublisherAmerican Society of Mechanical Engineers (ASME)
ISBN (Electronic)9780791851760
DOIs
StatePublished - Jan 1 2018
EventASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2018 - Quebec City, Canada
Duration: Aug 26 2018Aug 29 2018

Publication series

NameProceedings of the ASME Design Engineering Technical Conference
Volume2B-2018

Other

OtherASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2018
CountryCanada
CityQuebec City
Period8/26/188/29/18

All Science Journal Classification (ASJC) codes

  • Mechanical Engineering
  • Computer Graphics and Computer-Aided Design
  • Computer Science Applications
  • Modeling and Simulation

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  • Cite this

    Cunningham, J., & Tucker, C. S. (2018). A validation neural network (VNN) metamodel for predicting the performance of deep generative designs. In 44th Design Automation Conference (Proceedings of the ASME Design Engineering Technical Conference; Vol. 2B-2018). American Society of Mechanical Engineers (ASME). https://doi.org/10.1115/DETC201886299