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Deep reliability learning with latent adaptation for design optimization under uncertainty · Study

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ArticleDeep reliability learning with latent adaptation for design optimization under uncertainty

Mingyang Li2022

Article2022

Deep reliability learning with latent adaptation for design optimization under uncertainty

Mingyang Li · Zequn Wang

25 citations·0/0 files stored·Computer Methods in Applied Mechanics and Engineering

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Probabilistic and Robust Engineering Design Geotechnical Engineering and Analysis Gaussian Processes and Bayesian Inference

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References

1974Exact and Invariant Second-Moment Code Format 2016LIF: A new Kriging based learning function and its application to structural reliability analysis 2014Review and application of Artificial Neural Networks models in reliability analysis of steel structures 2012Accelerated subset simulation with neural networks for reliability analysis 2016Rare-event probability estimation with adaptive support vector regression surrogates 2006A direct decoupling approach for efficient reliability-based design optimization 2014Efficient structural reliability analysis method based on advanced Kriging model 2008Inverse analysis method using MPP-based dimension reduction for reliability-based design optimization of nonlinear and multi-dimensional systems

Cited by

2024A novel decoupled approach combining invertible cross-entropy method with Gaussian process modeling for reliability-based design and topology optimization 2023Surrogate modeling for high dimensional uncertainty propagation via deep kernel polynomial chaos expansion 2025Time series clustering adaptive enhanced method for time-dependent reliability analysis and design optimization 2022Decoupled reliability-based design optimization with a double-step modified adaptive chaos control approach

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Details

Year2022

Typearticle

Published2022-06-09

VenueComputer Methods in Applied Mechanics and Engineering

ISSN-L0045-7825

Languageen

Citations25

Files0 public / 0

ShelfProbabilistic and Robust Engineering Design

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doi10.1016/j.cma.2022.115130

openalex_workW4281645827

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