Neural networks for topology optimization
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Ivan Sosnovik
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.
Funding: This study was supported by the Ministry of Education and Science of the Russian Federation (grant 14.756.31.0001).
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© 2019 Walter de Gruyter GmbH, Berlin/Boston
Artikel in diesem Heft
- Frontmatter
- Determinism versus randomness in plankton dynamics: The analysis of noisy time series based on the recurrence plots
- Stochastic and deterministic kinetic energy backscatter parameterizations for simulation of the two-dimensional turbulence
- Neural networks for topology optimization
- Eulerian modelling of compressible three-fluid flows with surface tension
Artikel in diesem Heft
- Frontmatter
- Determinism versus randomness in plankton dynamics: The analysis of noisy time series based on the recurrence plots
- Stochastic and deterministic kinetic energy backscatter parameterizations for simulation of the two-dimensional turbulence
- Neural networks for topology optimization
- Eulerian modelling of compressible three-fluid flows with surface tension
