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    Representation Learning for Sequential Volumetric Design Tasks

    Source: Journal of Mechanical Design:;2024:;volume( 147 ):;issue: 005::page 51704-1
    Author:
    Alam, Md Ferdous
    ,
    Wang, Yi
    ,
    Cheng, Chin-Yi
    ,
    Luo, Jieliang
    DOI: 10.1115/1.4066686
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Volumetric design, also called massing design, is the first and critical step in professional building design, which is sequential in nature. As the volumetric design process requires careful design decisions and iterative adjustments, the underlying sequential design process encodes valuable information for designers. Many efforts have been made to automatically generate reasonable volumetric designs, but the quality of the generated design solutions varies, and evaluating a design solution requires either a prohibitively comprehensive set of metrics or expensive human expertise. While previous approaches focused on learning only the final design instead of sequential design tasks, we propose to encode the design knowledge from a collection of expert or high-performing design sequences and extract useful representations using transformer-based models. Later we propose to utilize the learned representations for crucial downstream applications such as design preference evaluation and procedural design generation. We develop the preference model by estimating the density of the learned representations, whereas we train an autoregressive transformer model for sequential design generation. We demonstrate our ideas by leveraging a novel dataset of thousands of sequential volumetric designs. Our preference model can compare two arbitrarily given design sequences and is almost 90% accurate in evaluation against random design sequences. Our autoregressive model is also capable of autocompleting a volumetric design sequence from a partial design sequence.
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      Representation Learning for Sequential Volumetric Design Tasks

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4306618
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    contributor authorAlam, Md Ferdous
    contributor authorWang, Yi
    contributor authorCheng, Chin-Yi
    contributor authorLuo, Jieliang
    date accessioned2025-04-21T10:38:53Z
    date available2025-04-21T10:38:53Z
    date copyright11/18/2024 12:00:00 AM
    date issued2024
    identifier issn1050-0472
    identifier othermd_147_5_051704.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4306618
    description abstractVolumetric design, also called massing design, is the first and critical step in professional building design, which is sequential in nature. As the volumetric design process requires careful design decisions and iterative adjustments, the underlying sequential design process encodes valuable information for designers. Many efforts have been made to automatically generate reasonable volumetric designs, but the quality of the generated design solutions varies, and evaluating a design solution requires either a prohibitively comprehensive set of metrics or expensive human expertise. While previous approaches focused on learning only the final design instead of sequential design tasks, we propose to encode the design knowledge from a collection of expert or high-performing design sequences and extract useful representations using transformer-based models. Later we propose to utilize the learned representations for crucial downstream applications such as design preference evaluation and procedural design generation. We develop the preference model by estimating the density of the learned representations, whereas we train an autoregressive transformer model for sequential design generation. We demonstrate our ideas by leveraging a novel dataset of thousands of sequential volumetric designs. Our preference model can compare two arbitrarily given design sequences and is almost 90% accurate in evaluation against random design sequences. Our autoregressive model is also capable of autocompleting a volumetric design sequence from a partial design sequence.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleRepresentation Learning for Sequential Volumetric Design Tasks
    typeJournal Paper
    journal volume147
    journal issue5
    journal titleJournal of Mechanical Design
    identifier doi10.1115/1.4066686
    journal fristpage51704-1
    journal lastpage51704-12
    page12
    treeJournal of Mechanical Design:;2024:;volume( 147 ):;issue: 005
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian