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Uncertainty Quantification of RF Circuits Using Stochastic Collocation Techniques
16 references·0 citing·37 second hop
Uncertainty Quantification of RF Circuits Using Stochastic Collocation Techniques (2022)Uncertainty Quantification of RF Circuits…Uncertainpy: A Python Toolbox for Uncertainty Quantification and Sensitivity Analysis in Computational Neuroscience (2018)Uncertainpy: A Python Toolbox…Review of Polynomial Chaos-Based Methods for Uncertainty Quantification in Modern Integrated Circuits (2018)Review of Polynomial Chaos-Ba…Stochastic transmission line analysis via polynomial chaos methods: an overview (2017)Stochastic transmission line …Sparse polynomial chaos expansions of frequency response functions using stochastic frequency transformation (2017)Sparse polynomial chaos expan…Handbook of Uncertainty Quantification (2017)Handbook of Uncertainty Quant…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…Sparse Linear Regression (SPLINER) Approach for Efficient Multidimensional Uncertainty Quantification of High-Speed Circuits (2016)Sparse Linear Regression (SPL…Generalized Decoupled Polynomial Chaos for Nonlinear Circuits With Many Random Parameters (2015)Generalized Decoupled Polynom…UQLab: A Framework for Uncertainty Quantification in Matlab (2014)UQLab: A Framework for Uncert…Stochastic Testing Method for Transistor-Level Uncertainty Quantification Based on Generalized Polynomial Chaos (2013)Stochastic Testing Method for…Stochastic collocation and stochastic Galerkin methods for linear differential algebraic equations (2013)Stochastic collocation and st…Data-driven uncertainty quantification using the arbitrary polynomial chaos expansion (2012)Data-driven uncertainty quant…Fast Numerical Methods for Stochastic Computations: A Review (2009)Fast Numerical Methods for St…Comparison of Non-Intrusive Polynomial Chaos and Stochastic Collocation Methods for Uncertainty Quantification (2009)Comparison of Non-Intrusive P…Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems (2003)Latin hypercube sampling and …The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations (2002)The Wiener--Askey Polynomial …Structural reliability under combined random load sequences (1978)High-Order Collocation Methods for Differential Equations with Random Inputs (2005)Mixed aleatory-epistemic uncertainty quantification with stochastic expansions and optimization-based interval estimation (2011)Dakota, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis version 6.0 theory manual (2014)First order reliability method for time-variant problems using series expansions (2014)Stochastic Finite Elements: A Spectral Approach (1991)Global sensitivity analysis using polynomial chaos expansions (2007)Adaptive sparse polynomial chaos expansion based on least angle regression (2010)An active-learning algorithm that combines sparse polynomial chaos expansions and bootstrap for structural reliability analysis (2018)The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support (2020)Toward SALib 2.0: Advancing the accessibility and interpretability of global sensitivity analyses (2022)Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates (2001)An introduction to sensitivity assessment of simulation models (2015)Reliability analysis with stratified importance sampling based on adaptive Kriging (2020)Empirical Model Building and Response Surfaces (1988)Design and Analysis of Computer Experiments (1989)A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code (2000)A new uncertainty importance measure (2006)Survey of sampling-based methods for uncertainty and sensitivity analysis (2006)A Review on Global Sensitivity Analysis Methods (2015)Large Sample Properties of Simulations Using Latin Hypercube Sampling (1987)Simulation-based optimal Bayesian experimental design for nonlinear systems (2012)A GF-discrepancy for point selection in stochastic seismic response analysis of structures with uncertain parameters (2015)POLYNOMIAL-CHAOS-BASED KRIGING (2015)Factorial Sampling Plans for Preliminary Computational Experiments (1991)Global Sensitivity Analysis. 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References

  • 2002The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations4,821 cited
  • 2003Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems2,707 cited
  • 2014UQLab: A Framework for Uncertainty Quantification in Matlab1,018 cited
  • 2009Fast Numerical Methods for Stochastic Computations: A Review608 cited
  • 2012Data-driven uncertainty quantification using the arbitrary polynomial chaos expansion451 cited
  • 2009Comparison of Non-Intrusive Polynomial Chaos and Stochastic Collocation Methods for Uncertainty Quantification348 cited
  • 2017Handbook of Uncertainty Quantification329 cited
  • 2013Stochastic Testing Method for Transistor-Level Uncertainty Quantification Based on Generalized Polynomial Chaos182 cited
  • 2018Review of Polynomial Chaos-Based Methods for Uncertainty Quantification in Modern Integrated Circuits121 cited
  • 2018Uncertainpy: A Python Toolbox for Uncertainty Quantification and Sensitivity Analysis in Computational Neuroscience111 cited
  • 2015Generalized Decoupled Polynomial Chaos for Nonlinear Circuits With Many Random Parameters102 cited
  • 2017Stochastic transmission line analysis via polynomial chaos methods: an overview82 cited
  • 2016Sparse Linear Regression (SPLINER) Approach for Efficient Multidimensional Uncertainty Quantification of High-Speed Circuits68 cited
  • 2017

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Sparse polynomial chaos expansions of frequency response functions using stochastic frequency transformation
66 cited
  • 2013Stochastic collocation and stochastic Galerkin methods for linear differential algebraic equations42 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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