Corpus ID: 221655441

Accelerating gradient-based topology optimization design with dual-model neural networks

@article{Qian2020AcceleratingGT,
  title={Accelerating gradient-based topology optimization design with dual-model neural networks},
  author={Chaojun Qian and Wenjing Ye},
  journal={ArXiv},
  year={2020},
  volume={abs/2009.06245}
}
Topology optimization (TO) is a common technique used in free-form designs. However, conventional TO-based design approaches suffer from high computational cost due to the need for repetitive forward calculations and/or sensitivity analysis, which are typically done using high-dimensional simulations such as Finite Element Analysis (FEA). In this work, neural networks are used as efficient surrogate models for forward and sensitivity calculations in order to greatly accelerate the design… Expand
Scalable Deep-Learning-Accelerated Topology Optimization for Additively Manufactured Materials
TLDR
A general scalable deep-learning (DL) based TO framework, referred to as SDL-TO, which utilizes parallel schemes in high performance computing (HPC) to accelerate the TO process for designing additively manufactured (AM) materials and significantly reduces the computational cost. Expand

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