MIT Libraries logoDSpace@MIT

MIT
View Item 
  • DSpace@MIT Home
  • MIT Open Access Articles
  • MIT Open Access Articles
  • View Item
  • DSpace@MIT Home
  • MIT Open Access Articles
  • MIT Open Access Articles
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Automatic hyperparameter tuning of topology optimization algorithms using surrogate optimization

Author(s)
Ha, Dat; Carstensen, Josephine
Thumbnail
Download158_2024_Article_3850.pdf (2.278Mb)
Publisher with Creative Commons License

Publisher with Creative Commons License

Creative Commons Attribution

Terms of use
Creative Commons Attribution https://creativecommons.org/licenses/by/4.0/
Metadata
Show full item record
Abstract
This paper presents a new approach that automates the tuning process in topology optimization of parameters that are traditionally defined by the user. The new method draws inspiration from hyperparameter optimization in machine learning. A new design problem is formulated where the topology optimization hyperparameters are defined as design variables and the problem is solved by surrogate optimization. The new design problem is nested, such that a topology optimization problem is solved as an inner problem. To encourage the identification of high-performing solutions while limiting the computational resource requirements, the outer objective function is defined as the original objective combined with penalization for intermediate densities and deviations from the prescribed material consumption. The contribution is demonstrated on density-based topology optimization with various hyperparameters and objectives, including compliance minimization, compliant mechanism design, and buckling load factor maximization. Consistent performance is observed across all tested examples. For a simple two hyperparameter case, the new framework is shown to reduce amount of times a topology optimization algorithm is executed by 90% without notably sacrificing the objective compared to a rigorous manual grid search.
Date issued
2024-09-04
URI
https://hdl.handle.net/1721.1/156697
Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Journal
Structural and Multidisciplinary Optimization
Publisher
Springer Berlin Heidelberg
Citation
Ha, D., Carstensen, J. Automatic hyperparameter tuning of topology optimization algorithms using surrogate optimization. Struct Multidisc Optim 67, 157 (2024).
Version: Final published version

Collections
  • MIT Open Access Articles

Browse

All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

My Account

Login

Statistics

OA StatisticsStatistics by CountryStatistics by Department
MIT Libraries
PrivacyPermissionsAccessibilityContact us
MIT
Content created by the MIT Libraries, CC BY-NC unless otherwise noted. Notify us about copyright concerns.