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A High-Dimensional Parameter Identification Method for Pipelines Based on Static Strain and DNN Surrogate Models to Accelerate Langevin Bayesian Inference.

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Title: A High-Dimensional Parameter Identification Method for Pipelines Based on Static Strain and DNN Surrogate Models to Accelerate Langevin Bayesian Inference.
Authors: Chen, Li1,2, Wu, Zhifeng2,3, zhifengwu@wit.edu.cn, Liu, Yanwen3, Li, Zhiyong4
Source: Buildings (2075-5309); Dec2025, Vol. 15 Issue 23, p4254, 27p
Database: Applied Science & Technology Source
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Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 190531704
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  Data: A High-Dimensional Parameter Identification Method for Pipelines Based on Static Strain and DNN Surrogate Models to Accelerate Langevin Bayesian Inference.
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  Data: <searchLink fieldCode="JN" term="%22Buildings+%282075-5309%29%22">Buildings (2075-5309)</searchLink>; Dec2025, Vol. 15 Issue 23, p4254, 27p
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      – Type: doi
        Value: 10.3390/buildings15234254
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      – Code: eng
        Text: English
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        PageCount: 27
        StartPage: 4254
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      – TitleFull: A High-Dimensional Parameter Identification Method for Pipelines Based on Static Strain and DNN Surrogate Models to Accelerate Langevin Bayesian Inference.
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            NameFull: Chen, Li
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            NameFull: Wu, Zhifeng
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            NameFull: Liu, Yanwen
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            – D: 01
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              Text: Dec2025
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              Y: 2025
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