Shape Reconstruction Optimization

Shape Reconstruction Optimization is a shape optimization method that specifically seeks to determine an optimal geometric configuration by minimizing discrepancies between observed data and numerically simulated boundary or field responses, distinguished from other shape optimization approaches by its primary focus on inferring or reconstructing a target shape from indirect, often incomplete or noisy, measurements through iterative refinement.

39

Publications

1.6K

Citations

121

Authors

58

Institutions

Publications per year

2017–2026

13

Authors

121

  1. MM
    M. Masry

    Cornell University, United States

    2 publications · 56 citations

    2

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  2. HA
    Habib Ammari

    École Polytechnique, France

    2 publications · 66 citations

    2

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  3. KK
    Kenichi Kanatani

    Gunma University, Japan

    2 publications · 84 citations

    2

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  4. HL
    Hod Lipson

    Cornell University, United States

    2 publications · 56 citations

    2

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  5. ML
    Marc Lambert

    Centre National de la Recherche Scientifique, France

    1 publications · 42 citations

    1

    h-index

1–5 of 121

Venues

  1. 2

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  2. IEICE Transactions on Information and Systems

    1 publications · 17 citations

    1

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  3. IEEE Transactions on Neural Networks

    1 publications · 29 citations

    1

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  4. Multimedia Tools and Applications

    1 publications · 10 citations

    1

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  5. Chemical Engineering Science

    1 publications · 86 citations

    1

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1–5