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    A Parametric Level Set Method for Topology Optimization Based on Deep Neural Network

    Source: Journal of Mechanical Design:;2021:;volume( 143 ):;issue: 009::page 091702-1
    Author:
    Deng, Hao
    ,
    To, Albert C.
    DOI: 10.1115/1.4050105
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: This paper proposes a new parametric level set method for topology optimization based on deep neural network (DNN). In this method, the fully connected DNN is incorporated into the conventional level set methods to construct an effective approach for structural topology optimization. The implicit function of level set is described by fully connected DNNs. A DNN-based level set optimization method is proposed, where the Hamilton–Jacobi partial differential equations (PDEs) are transformed into parametrized ordinary differential equations (ODEs). The zero-level set of implicit function is updated through updating the weights and biases of networks. The parametrized reinitialization is applied periodically to prevent the implicit function from being too steep or too flat in the vicinity of its zero-level set. The proposed method is implemented in the framework of minimum compliance, which is a well-known benchmark for topology optimization. In practice, designers desire to have multiple design options, where they can choose a better conceptual design base on their design experience. One of the major advantages of the DNN-based level set method is capable to generate diverse and competitive designs with different network architectures. Several numerical examples are presented to verify the effectiveness of the proposed DNN-based level set method.
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      A Parametric Level Set Method for Topology Optimization Based on Deep Neural Network

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    contributor authorDeng, Hao
    contributor authorTo, Albert C.
    date accessioned2022-02-05T21:48:16Z
    date available2022-02-05T21:48:16Z
    date copyright3/18/2021 12:00:00 AM
    date issued2021
    identifier issn1050-0472
    identifier othermd_143_9_091702.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4276371
    description abstractThis paper proposes a new parametric level set method for topology optimization based on deep neural network (DNN). In this method, the fully connected DNN is incorporated into the conventional level set methods to construct an effective approach for structural topology optimization. The implicit function of level set is described by fully connected DNNs. A DNN-based level set optimization method is proposed, where the Hamilton–Jacobi partial differential equations (PDEs) are transformed into parametrized ordinary differential equations (ODEs). The zero-level set of implicit function is updated through updating the weights and biases of networks. The parametrized reinitialization is applied periodically to prevent the implicit function from being too steep or too flat in the vicinity of its zero-level set. The proposed method is implemented in the framework of minimum compliance, which is a well-known benchmark for topology optimization. In practice, designers desire to have multiple design options, where they can choose a better conceptual design base on their design experience. One of the major advantages of the DNN-based level set method is capable to generate diverse and competitive designs with different network architectures. Several numerical examples are presented to verify the effectiveness of the proposed DNN-based level set method.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleA Parametric Level Set Method for Topology Optimization Based on Deep Neural Network
    typeJournal Paper
    journal volume143
    journal issue9
    journal titleJournal of Mechanical Design
    identifier doi10.1115/1.4050105
    journal fristpage091702-1
    journal lastpage091702-9
    page9
    treeJournal of Mechanical Design:;2021:;volume( 143 ):;issue: 009
    contenttypeFulltext
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