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Controlled Stratification Based on Kriging Surrogate Model: An Algorithm for Determining Extreme Quantiles in Electromagnetic Compatibility Risk Analysis
13 references·0 citing·42 second hop
Controlled Stratification Based on Kriging Surrogate Model: An Algorithm for Determining Extreme Quantiles in Electromagnetic Compatibility Risk Analysis (2019)Controlled Stratification Based on Krigin…Variability Impact of Many Design Parameters: The Case of a Realistic Electronic Link (2017)Variability Impact of Many De…Surrogate models for uncertainty quantification: An overview (2017)Surrogate models for uncertai…The Adaptive Controlled Stratification Method Applied to the Determination of Extreme Interference Levels in EMC Modeling With Uncertain Input Variables (2016)The Adaptive Controlled Strat…Reliability and Sensitivity Analysis of Transmission Lines in a Probabilistic EMC Context (2016)Reliability and Sensitivity A…Use of Adaptive Kriging Metamodeling in Reliability Analysis of Radiated Susceptibility in Coaxial Shielded Cables (2015)Use of Adaptive Kriging Metam…Uncertainty analysis of the specific absorption rate induced in a phantom using a stochastic spectral collocation method (2011)Uncertainty analysis of the s…Parameters Variability Effects on Multiconductor Interconnects via Hermite Polynomial Chaos (2011)Parameters Variability Effect…Adaptive sparse polynomial chaos expansion based on least angle regression (2010)Adaptive sparse polynomial ch…Controlled stratification for quantile estimation (2008)Controlled stratification for…Computational Modeling of Uncertainty in Time-Domain Electromagnetics (2006)Computational Modeling of Unc…On Latin hypercube sampling for structural reliability analysis (2002)On Latin hypercube sampling f…Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates (2001)Global sensitivity indices fo…Estimation of small failure probabilities in high dimensions by subset simulation (2001)Estimation of small failure p…Simulation and the Monte Carlo Method (1981)The stochastic finite element method: Past, present and future (2008)A combined Importance Sampling and Kriging reliability method for 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uncertainty: accomplishments and challenges (2010)Design and Analysis of Computer Experiments (1989)Design and Modeling for Computer Experiments (2005)Adaptive Optimal Allocation in Stratified Sampling Methods (2008)A survey of rare event simulation methods for static input–output models (2014)Bayesian Subset Simulation (2017)Numerical Challenges in the Use of Polynomial Chaos Representations for Stochastic Processes (2004)Stochastic Modeling-Based Variability Analysis of On-Chip Interconnects (2012)Variability Analysis of Multiport Systems Via Polynomial-Chaos Expansion (2012)Random-Space Dimensionality Reduction for Expedient Yield Estimation of Passive Microwave Structures (2013)Modeling uncertainty in flow simulations via generalized polynomial chaos (2003)High-Order Collocation Methods for Differential Equations with Random Inputs (2005)An efficient SFE method using Lagrange polynomials: Application to nonlinear mechanical problems with uncertain parameters (2007)Numerical analysis of the Burgers’ equation in the presence of uncertainty (2009)Analytic regularity and collocation approximation for elliptic PDEs with random domain deformations (2016)Second‐Order Reliability Approximations (1987)Structural Reliability Methods (1996)Global Sensitivity Analysis Using a Kriging Metamodel for EM Design Problems With Functional Outputs (2022)Global sensitivity analysis using polynomial chaos expansions (2007)Uncertainty in cardiac myofiber orientation and stiffnesses dominate the variability of left ventricle deformation response (2019)An active learning method combining deep neural network and weighted sampling for structural reliability analysis (2020)Surrogate-assisted global sensitivity analysis: an overview (2020)A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output From a Computer Code (2000)A Data Compression Strategy for the Efficient Uncertainty Quantification of Time-Domain Circuit Responses (2020)A Probabilistic Machine Learning Approach for the Uncertainty Quantification of Electronic Circuits Based on Gaussian Process Regression (2021)A new uncertainty importance measure (2006)Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index (2009)SALib: An open-source Python library for Sensitivity Analysis (2017)
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References

  • 2001Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates6,377 cited
  • 2001Estimation of small failure probabilities in high dimensions by subset simulation2,419 cited
  • 2010Adaptive sparse polynomial chaos expansion based on least angle regression1,507 cited
  • 2002On Latin hypercube sampling for structural reliability analysis798 cited
  • 2017Surrogate models for uncertainty quantification: An overview188 cited
  • 2006Computational Modeling of Uncertainty in Time-Domain Electromagnetics119 cited
  • 2011Parameters Variability Effects on Multiconductor Interconnects via Hermite Polynomial Chaos97 cited
  • 2008Controlled stratification for quantile estimation68 cited
  • 2017Variability Impact of Many Design Parameters: The Case of a Realistic Electronic Link45 cited
  • 2016Reliability and Sensitivity Analysis of Transmission Lines in a Probabilistic EMC Context33 cited
  • 2011Uncertainty analysis of the specific absorption rate induced in a phantom using a stochastic spectral collocation method31 cited
  • 2015Use of Adaptive Kriging Metamodeling in Reliability Analysis of Radiated Susceptibility in Coaxial Shielded Cables26 cited
  • 2016The Adaptive Controlled Stratification Method Applied to the Determination of Extreme Interference Levels in EMC Modeling With Uncertain Input Variables22 cited

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