MODELING AND OPTIMIZING MULTI-STAGE DESIGN WITH GAUSSIAN PROCESS BASED ON SURROGATE MODEL CHAIN

Siyu Yang, Liangyue Jia, Jia Hao, Reza Alizadeh

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Continued progress in the surrogate-model-based evaluation for the single-stage has been explored, but multistage has higher dimension and uncertainty. High dimension and low overall data of multi-stage leads to low accuracy of prediction, and cannot characterize the uncertainty of the final prediction performance. We propose a Gaussian Process-based surrogate model chain (GP-SMC) to evaluate the performance of multi-stage. Also, we combine the GP-SMC with the quasi-Newton method (L-BFGS-B), make full use of the gradient information of the GP-SMC to get an optimization solution rapidly. The MAE (Mean Absolute Error) and MRE (Mean Relative Error) and STD (standard deviation) of GP-SMC's predicted value are 10% of the prediction of a single surrogate model, which achieves a significant improvement in prediction accuracy and a significant reduction in uncertainty. Compared with the original optimization results, the average performance is improved by 21.05%. Based on the optimal solution and GP-SMC, the confidence interval of the final performance under the optimal solution is obtained, and the confidence level is 99%. The truth probability of GP-SMC is 91.25% in the test dataset, which is higher than single GP’s 85% truth probability. The technology is used in the case of Hot Rod Rolling, and can also be applied to complex product design with multi-stage.

Original languageEnglish
Title of host publication48th Design Automation Conference (DAC)
PublisherAmerican Society of Mechanical Engineers (ASME)
ISBN (Electronic)9780791886236
DOIs
Publication statusPublished - 2022
EventASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2022 - St. Louis, United States
Duration: 14 Aug 202217 Aug 2022

Publication series

NameProceedings of the ASME Design Engineering Technical Conference
Volume3-B

Conference

ConferenceASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2022
Country/TerritoryUnited States
CitySt. Louis
Period14/08/2217/08/22

Keywords

  • Design optimization
  • Gaussian Process
  • Multi-stage
  • Surrogate Model
  • Surrogate Model Chain

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