Two-stage convolutional encoder-decoder network to improve the performance and reliability of deep learning models for topology optimization

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 w...

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Vydáno v:Structural and multidisciplinary optimization Ročník 63; číslo 4; s. 1927 - 1950
Hlavní autoři: Ates, Gorkem Can, Gorguluarslan, Recep M.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2021
Springer Nature B.V
Témata:
Artificial neural networks
Coders
Computational Mathematics and Numerical Analysis
Deep learning
Encoders-Decoders
Engineering
Engineering Design
Error reduction
Iterative methods
Network reliability
Network topologies
Optimization
Performance enhancement
Performance prediction
Research Paper
Theoretical and Applied Mechanics
Topology optimization
Deep learning
Topology optimization
Encoder and decoder network
Neural network
Convolutional neural network
ISSN:1615-147X, 1615-1488
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Shrnutí: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.
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ISSN:1615-147X
1615-1488
DOI:10.1007/s00158-020-02788-w