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    GAN-DUF: Hierarchical Deep Generative Models for Design Under Free-Form Geometric Uncertainty

    Source: Journal of Mechanical Design:;2022:;volume( 145 ):;issue: 001::page 11703-1
    Author:
    Chen, Wei (Wayne)
    ,
    Lee, Doksoo
    ,
    Balogun, Oluwaseyi
    ,
    Chen, Wei
    DOI: 10.1115/1.4055898
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Deep generative models have demonstrated effectiveness in learning compact and expressive design representations that significantly improve geometric design optimization. However, these models do not consider the uncertainty introduced by manufacturing or fabrication. The past work that quantifies such uncertainty often makes simplifying assumptions on geometric variations, while the “real-world,” “free-form” uncertainty and its impact on design performance are difficult to quantify due to the high dimensionality. To address this issue, we propose a generative adversarial network-based design under uncertainty framework (GAN-DUF), which contains a deep generative model that simultaneously learns a compact representation of nominal (ideal) designs and the conditional distribution of fabricated designs given any nominal design. This opens up new possibilities of (1) building a universal uncertainty quantification model compatible with both shape and topological designs, (2) modeling free-form geometric uncertainties without the need to make any assumptions on the distribution of geometric variability, and (3) allowing fast prediction of uncertainties for new nominal designs. We can combine the proposed deep generative model with robust design optimization or reliability-based design optimization for design under uncertainty. We demonstrated the framework on two real-world engineering design examples and showed its capability of finding the solution that possesses better performance after fabrication.
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      GAN-DUF: Hierarchical Deep Generative Models for Design Under Free-Form Geometric Uncertainty

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    contributor authorChen, Wei (Wayne)
    contributor authorLee, Doksoo
    contributor authorBalogun, Oluwaseyi
    contributor authorChen, Wei
    date accessioned2023-11-29T19:28:09Z
    date available2023-11-29T19:28:09Z
    date copyright10/31/2022 12:00:00 AM
    date issued10/31/2022 12:00:00 AM
    date issued2022-10-31
    identifier issn1050-0472
    identifier othermd_145_1_011703.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4294782
    description abstractDeep generative models have demonstrated effectiveness in learning compact and expressive design representations that significantly improve geometric design optimization. However, these models do not consider the uncertainty introduced by manufacturing or fabrication. The past work that quantifies such uncertainty often makes simplifying assumptions on geometric variations, while the “real-world,” “free-form” uncertainty and its impact on design performance are difficult to quantify due to the high dimensionality. To address this issue, we propose a generative adversarial network-based design under uncertainty framework (GAN-DUF), which contains a deep generative model that simultaneously learns a compact representation of nominal (ideal) designs and the conditional distribution of fabricated designs given any nominal design. This opens up new possibilities of (1) building a universal uncertainty quantification model compatible with both shape and topological designs, (2) modeling free-form geometric uncertainties without the need to make any assumptions on the distribution of geometric variability, and (3) allowing fast prediction of uncertainties for new nominal designs. We can combine the proposed deep generative model with robust design optimization or reliability-based design optimization for design under uncertainty. We demonstrated the framework on two real-world engineering design examples and showed its capability of finding the solution that possesses better performance after fabrication.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleGAN-DUF: Hierarchical Deep Generative Models for Design Under Free-Form Geometric Uncertainty
    typeJournal Paper
    journal volume145
    journal issue1
    journal titleJournal of Mechanical Design
    identifier doi10.1115/1.4055898
    journal fristpage11703-1
    journal lastpage11703-11
    page11
    treeJournal of Mechanical Design:;2022:;volume( 145 ):;issue: 001
    contenttypeFulltext
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