Neural networks for topology optimization

Ivan Sosnovik, Ivan Oseledets

    Research output: Contribution to journalArticlepeer-review

    63 Citations (Scopus)

    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.

    Original languageEnglish
    Pages (from-to)215-223
    Number of pages9
    JournalRussian Journal of Numerical Analysis and Mathematical Modelling
    Volume34
    Issue number4
    DOIs
    Publication statusPublished - 1 Aug 2019

    Keywords

    • Deep learning
    • image segmentation
    • topology optimization

    Fingerprint

    Dive into the research topics of 'Neural networks for topology optimization'. Together they form a unique fingerprint.

    Cite this