Neural networks for topology optimization
By Ivan Sosnovik, Ivan V. Oseledets. Russian Journal of Numerical Analysis and Mathematical Modelling, 2017
308 citations recorded by the source corpus.
Abstract In this research, we propose a deep learning based approach for speeding up the topology optimization methods. The problem we seek to solve is the layout problem. The main novelty of this work is to state the problem as an image segmentation task. We leverage the power of deep learning methods as the efficient pixel-wise image labeling technique to perform the topology optimization. We introduce convolutional encoder-decoder architecture and the overall approach of solving the above-described problem with high performance. The conducted experiments demonstrate the significant acceleration of the optimization process. The proposed approach has excellent generalization properties. We demonstrate the ability of the application of the proposed model to other problems. The successful results, as well as the drawbacks of the current method, are discussed.
Subfields: Civil and Structural Engineering, Geology, Industrial and Manufacturing Engineering
View Paper: https://www.semanticscholar.org/paper/6210aeb142a7075bee11c097579cbbf03d1f8119
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Citations in the corpus
- Deep Generative Models in Engineering Design: A Review (2021, methodology): “Plain NN [53]i [54]vi [55]p∗ [56]i [57]p [58]i [59]i”
- Deep learning for determining a near-optimal topological design without any iteration (2018, background): “Sosnovik and Oseledets (2017) adopted a deep learning-based image segmentation task to hasten topology optimization; their model learned about the mapping from an intermediate structure at each iteration to obtain the final optimized structure.”