CNN-based image recognition for topology optimization
作者：
Highlights：
• A new surrogated model is proposed to predict compliance information for topology optimization.
• The proposed method can eliminate the step of FEM and accelerate optimization processes.
• The CNNs are then introduced to train neural networks by using coarse elements.
• High resolution image can be predicted in the trained NNs by using resize interpolation methods.
• A GPU is then used to accelerate the bulk-processing of data.
摘要
•A new surrogated model is proposed to predict compliance information for topology optimization.•The proposed method can eliminate the step of FEM and accelerate optimization processes.•The CNNs are then introduced to train neural networks by using coarse elements.•High resolution image can be predicted in the trained NNs by using resize interpolation methods.•A GPU is then used to accelerate the bulk-processing of data.
论文关键词：Convolutional neural network,GPU computing,Topology analysis,Compliance,Topology image recognition
论文评审过程：Received 28 November 2019, Revised 27 February 2020, Accepted 6 April 2020, Available online 11 April 2020, Version of Record 13 May 2020.
论文官网地址：https://doi.org/10.1016/j.knosys.2020.105887
