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- W4309402859 abstract "In this paper, we propose a novel graded multiscale topology optimization framework by exploiting the unique classification capacity of neural networks. The salient features of this framework include: (1) the number of design variables is only weakly dependent on the number of pre-selected microstructures, (2) it guarantees partition of unity while discouraging microstructure mixing, (3) it supports automatic differentiation, thereby eliminating manual sensitivity analysis, and (4) it supports high-resolution re-sampling, leading to smoother variation of microstructure topologies. The proposed framework is illustrated through several examples." @default.
- W4309402859 created "2022-11-26" @default.
- W4309402859 creator A5001417775 @default.
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- W4309402859 date "2023-01-01" @default.
- W4309402859 modified "2023-10-01" @default.
- W4309402859 title "Graded multiscale topology optimization using neural networks" @default.
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- W4309402859 doi "https://doi.org/10.1016/j.advengsoft.2022.103359" @default.
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