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Stochastic Collocation Applications in Computational Electromagnetics
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Stochastic Collocation Applications in Computational Electromagnetics (2018)Stochastic Collocation Applications in Co…Sparse Linear Regression (SPLINER) Approach for Efficient Multidimensional Uncertainty Quantification of High-Speed Circuits (2016)Sparse Linear Regression (SPL…Performance Comparison of the SGM and the SCM in EMC Simulation (2016)Performance Comparison of the…Big-Data Tensor Recovery for High-Dimensional Uncertainty Quantification of Process Variations (2016)Big-Data Tensor Recovery for …Application of Different Methods to Quantify Uncertainty in Specific Absorption Rate Calculation Using a CAD-Based Mobile Phone Model (2016)Application of Different Meth…Efficient Statistical Simulation of Microwave Devices Via Stochastic Testing-Based Circuit Equivalents of Nonlinear Components (2015)Efficient Statistical Simulat…A Stochastic Collocation Method for Elliptic Partial Differential Equations with Random Input Data (2010)A Stochastic Collocation Meth…Stochastic Analysis of Electrostatic MEMS Subjected to Parameter Variations (2009)Stochastic Analysis of Electr…Fast Numerical Methods for Stochastic Computations: A Review (2009)Fast Numerical Methods for St…Stochastic Finite Elements: A Spectral Approach (1991)The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations (2002)High-Order Collocation Methods for Differential Equations with Random Inputs (2005)Recent Developments in Spectral Stochastic Methods for the Numerical Solution of Stochastic Partial Differential Equations (2009)An adaptive high-dimensional stochastic model representation technique for the solution of stochastic partial differential equations (2010)Multi-level Monte Carlo Finite Element method for elliptic PDEs with stochastic coefficients (2011)A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code (1979)A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code (2000)A data‐driven stochastic collocation approach for uncertainty quantification in MEMS (2010)Weighted Smolyak algorithm for solution of stochastic differential equations on non‐uniform probability measures (2010)High-Dimensional Uncertainty Quantification of Electronic and Photonic IC With Non-Gaussian Correlated Process Variations (2019)Theory of Optimal Experiments. (1972)Numerical Methods for Stochastic Computations: A Spectral Method Approach (2010)Stochastic Collocation With Non-Gaussian Correlated Process Variations: Theory, Algorithms, and Applications (2018)Numerical Solution of Stochastic Differential Equations (2016)Dimension-Reduced Sparse Grid Strategy for a Stochastic Collocation Method in EMC Software (2017)A Hybrid Perturbative-Stochastic Galerkin Method for the Variability Analysis of Nonuniform Transmission Lines (2019)A Perturbative Stochastic Galerkin Method for the Uncertainty Quantification of Linear Circuits (2020)Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code (1979)Global sensitivity analysis using polynomial chaos expansions (2007)A Statistical Parsimony Method for Uncertainty Quantification of FDTD Computation Based on the PCA and Ridge Regression (2019)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)Multidimensional Uncertainty Quantification of Microwave/RF Networks Using Linear Regression and Optimal Design of Experiments (2016)Accurate Reduced Dimensional Polynomial Chaos for Efficient Uncertainty Quantification of Microwave/RF Networks (2017)Probabilistic Uncertainty Quantification of Microwave Circuits Using Gaussian Processes (2022)A Probabilistic Machine Learning Approach for the Uncertainty Quantification of Electronic Circuits Based on Gaussian Process Regression (2021)
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References

  • 2009Fast Numerical Methods for Stochastic Computations: A Review608 cited
  • 2010A Stochastic Collocation Method for Elliptic Partial Differential Equations with Random Input Data400 cited
  • 2016Sparse Linear Regression (SPLINER) Approach for Efficient Multidimensional Uncertainty Quantification of High-Speed Circuits68 cited
  • 2016Big-Data Tensor Recovery for High-Dimensional Uncertainty Quantification of Process Variations66 cited
  • 2015Efficient Statistical Simulation of Microwave Devices Via Stochastic Testing-Based Circuit Equivalents of Nonlinear Components47 cited
  • 2009Stochastic Analysis of Electrostatic MEMS Subjected to Parameter Variations42 cited
  • 2016Application of Different Methods to Quantify Uncertainty in Specific Absorption Rate Calculation Using a CAD-Based Mobile Phone Model39 cited
  • 2016Performance Comparison of the SGM and the SCM in EMC Simulation34 cited

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