Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/7751
Title: Two-stage convolutional encoder-decoder network to improve the performance and reliability of deep learning models for topology optimization
Authors: Ateş, Görkem Can
Görgülüarslan, Recep Muhammet
Keywords: Deep learning
Neural network
Topology optimization
Convolutional neural network
Encoder and decoder network
Issue Date: 2021
Publisher: Springer
Abstract: A vital necessity when employing state-of-the-art deep neural networks (DNNs) for topology optimization is to predict near-optimal structures while satisfying pre-defined optimization constraints and objective function. Existing studies, on the other hand, suffer from the structural disconnections which result in unexpected errors in the objective and constraints. In this study, a two-stage network model is proposed using convolutional encoder-decoder networks that incorporate a new way of loss functions to reduce the number of structural disconnection cases as well as to reduce pixel-wise error to enhance the predictive performance of DNNs for topology optimization without any iteration. In the first stage, a single DNN model architecture is proposed and used in two parallel networks using two different loss functions for each called the mean square error (MSE) and mean absolute error (MAE). Once the priori information is generated from the first stage, it is instantly fed into the second stage, which acts as a rectifier network over the priori predictions. Finally, the second stage is trained using the binary cross-entropy (BCE) loss to provide the final predictions. The proposed two-stage network with the proposed loss functions is implemented for both two-dimensional (2D) and three-dimensional (3D) topology optimization datasets to observe its generalization ability. The validation results showed that the proposed two-stage framework could improve network prediction ability compared to a single network while significantly reducing compliance and volume fraction errors.
URI: https://doi.org/10.1007/s00158-020-02788-w
https://hdl.handle.net/20.500.11851/7751
ISSN: 1615-147X
1615-1488
Appears in Collections:Makine Mühendisliği Bölümü / Department of Mechanical Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Show full item record

CORE Recommender

SCOPUSTM   
Citations

3
checked on Sep 23, 2022

WEB OF SCIENCETM
Citations

15
checked on Sep 24, 2022

Page view(s)

56
checked on Dec 26, 2022

Google ScholarTM

Check

Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.