Clayton G. Webster

发表

Compressed Sensing Approaches for Polynomial Approximation of High-Dimensional Functions

Ben Adcock, Simone Brugiapaglia, Clayton G. Webster, 2017, 1703.06987.

Explicit cost bounds of stochastic Galerkin approximations for parameterized PDEs with random coefficients

Guannan Zhang, Clayton G. Webster, Nick C. Dexter, 2015, Comput. Math. Appl..

Numerical methods for a class of nonlocal diffusion problems with the use of backward SDEs

Weidong Zhao, Guannan Zhang, Max D. Gunzburger, 2016, Comput. Math. Appl..

Analysis of quasi-optimal polynomial approximations for parameterized PDEs with deterministic and stochastic coefficients

Guannan Zhang, Hoang Tran, Clayton G. Webster, 2015, Numerische Mathematik.

A Multilevel Stochastic Collocation Method for Partial Differential Equations with Random Input Data

Max Gunzburger, Clayton G. Webster, Aretha L. Teckentrup, 2014, SIAM/ASA J. Uncertain. Quantification.

An adaptive stochastic gradient-free approach for high-dimensional blackbox optimization pdf

Anton Dereventsov, Clayton G. Webster, Joseph D. Daws, 2020, ArXiv.

Evolve Then Filter Regularization for Stochastic Reduced Order Modeling

Feng Bao, Clayton G. Webster, Xuping Xie, 2018 .

Uncertainty quantification techniques for population density estimates derived from sparse open source data

Robert N. Stewart, Clayton G. Webster, April Morton, 2013, Defense, Security, and Sensing.

Stochastic finite element methods for partial differential equations with random input data*

Guannan Zhang, Max Gunzburger, Clayton G. Webster, 2014, Acta Numerica.

An adaptive sparse-grid iterative ensemble Kalman filter approach for parameter field estimation

Guannan Zhang, Max D. Gunzburger, Clayton G. Webster, 2014, Int. J. Comput. Math..

An Anisotropic Sparse Grid Stochastic Collocation Method for Partial Differential Equations with Random Input Data

Fabio Nobile, Raúl Tempone, Clayton G. Webster, 2008, SIAM J. Numer. Anal..

Sparse Grid Quadrature Rules Based on Conformal Mappings

Clayton G. Webster, Peter Jantsch, 2018 .

A nonlocal feature-driven exemplar-based approach for image inpainting

Viktor Reshniak, Clayton G. Webster, Jeremy Trageser, 2019, SIAM J. Imaging Sci..

Closure Learning for Nonlinear Model Reduction Using Deep Residual Neural Network

Traian Iliescu, Xuping Xie, Clayton G. Webster, 2019, Fluids.

Uncertainty Quantification and High Performance Computing (Dagstuhl Seminar 16372)

Vincent Heuveline, Clayton G. Webster, Michael Schick, 2016, Dagstuhl Reports.

Hyperspherical Sparse Approximation Techniques for High-Dimensional Discontinuity Detection

Guannan Zhang, Max D. Gunzburger, Clayton G. Webster, 2016, SIAM Rev..

Reconstruction of jointly sparse vectors via manifold optimization pdf

Hoang Tran, Clayton G. Webster, Armenak Petrosyan, 2018, Applied Numerical Mathematics.

A Sparse Grid Stochastic Collocation Method for Partial Differential Equations with Random Input Data

Fabio Nobile, Raúl Tempone, Clayton G. Webster, 2008, SIAM J. Numer. Anal..

Numerical Analysis of Fixed Point Algorithms in the Presence of Hardware Faults

Clayton G. Webster, Miroslav Stoyanov, 2015, SIAM J. Sci. Comput..

A Hybrid Sparse-Grid Approach for Nonlinear Filtering Problems Based on Adaptive-Domain of the Zakai Equation Approximations

Guannan Zhang, Feng Bao, Yanzhao Cao, 2014, SIAM/ASA J. Uncertain. Quantification.

A Weighted $\ell_1$-Minimization Approach For Wavelet Reconstruction of Signals and Images pdf

Hoang Tran, Clayton G. Webster, Joseph Daws, 2019, 1909.07270.

A dynamically adaptive sparse grids method for quasi-optimal interpolation of multidimensional functions

Clayton G. Webster, Miroslav Stoyanov, C. Webster, 2016, Comput. Math. Appl..

A Polynomial-Based Approach for Architectural Design and Learning with Deep Neural Networks pdf

Clayton G. Webster, Joseph Daws, C. Webster, 2019, ArXiv.

A Hyperspherical Adaptive Sparse-Grid Method for High-Dimensional Discontinuity Detection

Guannan Zhang, Max D. Gunzburger, Clayton G. Webster, 2015, SIAM J. Numer. Anal..

Design Under Uncertainty Employing Stochastic Expansion Methods

Michael S. Eldred, Paul G. Constantine, Clayton G. Webster, 2008 .

Accelerating Stochastic Collocation Methods for Partial Differential Equations with Random Input Data

Guannan Zhang, Clayton G. Webster, Peter Jantsch, 2015, SIAM/ASA J. Uncertain. Quantification.

Non-Intrusive Inference Reduced Order Model for Fluids Using Deep Multistep Neural Network

Guannan Zhang, Xuping Xie, Clayton G. Webster, 2019 .

Polynomial approximation via compressed sensing of high-dimensional functions on lower sets

Hoang Tran, Clayton G. Webster, Nick C. Dexter, 2016, Math. Comput..

DOE Advanced Scientific Computing Advisory Subcommittee (ASCAC) Report: Top Ten Exascale Research Challenges

John Shalf, Jack Dongarra, Vivek Sarkar, 2014 .

LDRD Final Report: Capabilities for Uncertainty in Predictive Science.

Andrew G. Salinger, Clayton G. Webster, Michael S. Eldred, 2008 .

Realizing Exascale Performance for Uncertainty Quantification.

Harold C. Edwards, Clayton G. Webster, Eric Todd Phipps, 2013 .

An adaptive stochastic gradient-free approach for high-dimensional blackbox optimization pdf

Anton Dereventsov, Clayton G. Webster, Joseph D. Daws, 2020, Proceedings of International Conference on Computational Intelligence.

Application of High Performance Computing for Simulating the Unstable Dynamics of Dilute Spark-Ignited Combustion

Charles E. A. Finney, Robert M. Wagner, Clayton G. Webster, 2014 .

Reduced basis methods for nonlocal diffusion problems with random input data

Guannan Zhang, Max D. Gunzburger, Clayton G. Webster, 2017 .

An anisotropic sparse grid stochastic collocation method for elliptic partial differential equations with random input data

Raul Tempone, Fabio Nobile, Clayton G. Webster, 2007 .

Non-Intrusive Inference Reduced Order Model for Fluids Using Deep Multistep Neural Network

Guannan Zhang, Xuping Xie, Clayton G. Webster, 2019, Mathematics.

Polynomial approximation of high-dimensional functions via compressed sensing

Ben Adcock, Simone Brugiapaglia, Clayton G. Webster, 2017 .

An Adaptive Wavelet Stochastic Collocation Method for Irregular Solutions of Partial Differential Equations with Random Input Data

Guannan Zhang, Max Gunzburger, Clayton G. Webster, 2014 .

A GENERALIZED STOCHASTIC COLLOCATION APPROACH TO CONSTRAINED OPTIMIZATION FOR RANDOM DATA IDENTIFICATION PROBLEMS

Max Gunzburger, Clayton G. Webster, 2013 .

An Efficient Surrogate Modeling Approach in Bayesian Uncertainty Analysis

Ming Ye, Guannan Zhang, Max Gunzburger, 2013 .

An Adaptive Wavelet Stochastic Collocation Method for Irregular Solutions of PDEs with Random Input Data

Guannan Zhang, Max Gunzburger, Clayton G. Webster, 2013 .

A GRADIENT-BASED SAMPLING APPROACH FOR DIMENSION REDUCTION OF PARTIAL DIFFERENTIAL EQUATIONS WITH STOCHASTIC COEFFICIENTS

Clayton G. Webster, Miroslav Stoyanov, C. Webster, 2015 .

On the Lebesgue constant of weighted Leja points for Lagrange interpolation on unbounded domains

Guannan Zhang, Clayton G. Webster, Peter Jantsch, 2016, IMA Journal of Numerical Analysis.

An adaptive wavelet stochastic collocation method for irregular solutions of stochastic partial differential equations

Guannan Zhang, Max Gunzburger, Clayton G. Webster, 2012 .

Evolve Filter Stabilization Reduced-Order Model for Stochastic Burgers Equation

Xuping Xie, Clayton G. Webster, Feng Bao, 2018, Fluids.

Sparse Collocation Methods for Stochastic Interpolation and Quadrature

Max D. Gunzburger, Clayton G. Webster, Guannan Zhang, 2017 .

An adaptive sparse‐grid high‐order stochastic collocation method for Bayesian inference in groundwater reactive transport modeling

Ming Ye, Guannan Zhang, Max Gunzburger, 2013 .

A Sparse Grid Method for Bayesian Uncertainty Quantification with Application to Large Eddy Simulation Turbulence Models

Guannan Zhang, Clayton G. Webster, C. Webster, 2014, 1508.01487.

A Dynamically Adaptive Sparse Grid Method for Quasi-Optimal Interpolation of Multidimensional Analytic Functions

Clayton G. Webster, C. Webster, M. Stoyanov, 2015, 1508.01125.

Sparse grid stochastic collocation techniques for the numerical solution of partial differential equations with random input data

Clayton G. Webster, 2007 .