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Physics-guided mixture density networks for uncertainty quantification · Study

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ArticlePhysics-guided mixture density networks for uncertainty quantification

Yu Yang2022

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Articlebronze2022

Physics-guided mixture density networks for uncertainty quantification

Yu Yang · Yongming Liu

41 citations·0/0 files stored·Reliability Engineering & System Safety

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Probabilistic and Robust Engineering Design Model Reduction and Neural Networks Gaussian Processes and Bayesian Inference

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References

2021Uncertainty quantification for Multiphase-CFD simulations of bubbly flows: a machine learning-based Bayesian approach supported by high-resolution experiments 2011Quantification of margins and uncertainties of complex systems in the presence of aleatoric and epistemic uncertainty 2011An efficient analytical Bayesian method for reliability and system response updating based on Laplace and inverse first-order reliability computations 2021Information fusion and machine learning for sensitivity analysis using physics knowledge and experimental data

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Details

Year2022

Typearticle

Published2022-09-14

VenueReliability Engineering & System Safety

ISSN-L0951-8320

Languageen

Citations41

Files0 public / 0

ShelfProbabilistic and Robust Engineering Design

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doi10.1016/j.ress.2022.108823

openalex_workW4295809799

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