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Physics-guided mixture density networks for uncertainty quantification
4 references·0 citing·24 second hopsampled
Physics-guided mixture density networks for uncertainty quantification (2022)Physics-guided mixture density networks f…Uncertainty quantification for Multiphase-CFD simulations of bubbly flows: a machine learning-based Bayesian approach supported by high-resolution experiments (2021)Uncertainty quantification fo…Information fusion and machine learning for sensitivity analysis using physics knowledge and experimental data (2021)Information fusion and machin…Quantification of margins and uncertainties of complex systems in the presence of aleatoric and epistemic uncertainty (2011)Quantification of margins and…An efficient analytical Bayesian method for reliability and system response updating based on Laplace and inverse first-order reliability computations (2011)An efficient analytical Bayes…Probability Concepts in Engineering Planning and Design (1975)On The Quantitative Definition of Risk (1981)Probability, reliability and statistical methods in engineering design (1999)An enhanced unified uncertainty analysis approach based on first order reliability method with single-level optimization (2013)Sequential Optimization and Mixed Uncertainty Analysis Method for Reliability-Based Optimization (2013)Quantification of margins and mixed uncertainties using evidence theory and stochastic expansions (2015)Exact and Invariant Second-Moment Code Format (1974)Structural reliability under combined random load sequences (1978)Improve the Accuracy of Asymptotic Approximation in Reliability Problems Involving Multimodal Distributions (2016)Probability assessments of identified parameters for stochastic structures using point estimation method (2016)Structural Reliability Analysis and Prediction (2017)An asymptotic stochastic response surface approach to reliability assessment under multi-source heterogeneous uncertainties (2021)A Bayesian calibration approach to the thermal problem (2008)Bayesian uncertainty quantification applied to RANS turbulence models (2011)Inverse uncertainty quantification using the modular Bayesian approach based on Gaussian process, Part 1: Theory (2018)Arbitrary multi-resolution multi-wavelet-based polynomial chaos expansion for data-driven uncertainty quantification (2022)Functional PCA and deep neural networks-based Bayesian inverse uncertainty quantification with transient experimental data (2024)Bayesian Optimized Deep Ensemble for Uncertainty Quantification of Deep Neural Networks: a System Safety Case Study on Sodium Fast Reactor Thermal Stratification Modeling (2025)Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates (2001)The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations (2002)Global Sensitivity Analysis. The Primer (2007)Deep adaptive arbitrary polynomial chaos expansion: A mini-data-driven semi-supervised method for uncertainty quantification (2022)Efficient global sensitivity analysis for high-dimensional outputs combining data-driven probability models and dimensionality reduction (2022)AI-augmented failure modes, effects, and criticality analysis (AI-FMECA) for industrial applications (2024)
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References

  • 2021Uncertainty quantification for Multiphase-CFD simulations of bubbly flows: a machine learning-based Bayesian approach supported by high-resolution experiments73 cited
  • 2011Quantification of margins and uncertainties of complex systems in the presence of aleatoric and epistemic uncertainty65 cited
  • 2011An efficient analytical Bayesian method for reliability and system response updating based on Laplace and inverse first-order reliability computations63 cited
  • 2021Information fusion and machine learning for sensitivity analysis using physics knowledge and experimental data51 cited

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