Speeding up Computational Morphogenesis with Online Neural Synthetic Gradients

Yuyu Zhang, Heng Chi, Binghong Chen, Tsz Ling Elaine Tang, Lucia Mirabella, Le Song, Glaucio H. Paulino

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

1 Scopus citations

Abstract

A wide range of modern science and engineering applications are formulated as optimization problems with a system of partial differential equations (PDEs) as constraints. These PDE-constrained optimization problems are typically solved in a standard discretize-then-optimize approach. In many industry applications that require high-resolution solutions, the discretized constraints can easily have millions or even billions of variables, making it very slow for the standard iterative optimizer to solve the exact gradients. In this work, we propose a general framework to speed up PDE-constrained optimization using online neural synthetic gradients (ONSG) with a novel two-scale optimization scheme. We successfully apply our ONSG framework to computational morphogenesis, a representative and challenging class of PDE-constrained optimization problems. Extensive experiments have demonstrated that our method can significantly speed up computational morphogenesis (also known as topology optimization), and meanwhile maintain the quality of final solution compared to the standard optimizer. On a large-scale 3D optimal design problem with around 1, 400, 000 design variables, our method achieves up to 7.5x speedup while producing optimized designs with comparable objectives.

Original languageEnglish (US)
Title of host publicationIJCNN 2021 - International Joint Conference on Neural Networks, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9780738133669
DOIs
StatePublished - Jul 18 2021
Externally publishedYes
Event2021 International Joint Conference on Neural Networks, IJCNN 2021 - Virtual, Shenzhen, China
Duration: Jul 18 2021Jul 22 2021

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2021-July

Conference

Conference2021 International Joint Conference on Neural Networks, IJCNN 2021
Country/TerritoryChina
CityVirtual, Shenzhen
Period7/18/217/22/21

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

Keywords

  • PDE-constrained optimization
  • computational morphogenesis
  • deep learning
  • neural synthetic gradients

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