C. Webster

发表

Evaluation of Non-Intrusive Approaches for Wiener-Askey Generalized Polynomial Chaos.

M. Eldred, C. Webster, P. Constantine, 2008 .

REDUCED ORDER MODELING OF SOME NONLINEAR STOCHASTIC PARTIAL DIFFERENTIAL EQUATIONS

J. Burkardt, M. Gunzburger, C. Webster, 2007 .

A class of null space conditions for sparse recovery via nonconvex, non-separable minimizations

Clayton Webster, Hoang Tran, C. Webster, 2017, 1710.07348.

Unified sufficient conditions for uniform recovery of sparse signals via nonconvex minimizations

C. Webster, Hoang Tran, 2017 .

Quantifying the Impact of Single Bit Flips on Floating Point Arithmetic

F. Mueller, C. Webster, M. Stoyanov, 2013 .

Evolve Filter Stabilization Reduced-Order Model for Stochastic Burgers Equation

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

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

Ming Ye, Guannan Zhang, Max Gunzburger, 2013 .

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 anisotropic sparse grid stochastic collocation method for PDEs with random input data.

Ivo Babuška, Raul Tempone, Fabio Nobile, 2007 .

A mixed ℓ1 regularization approach for sparse simultaneous approximation of parameterized PDEs

Clayton Webster, Nick Dexter, Hoang Tran, 2018, ESAIM: Mathematical Modelling and Numerical Analysis.

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 backward stochastic differential equations approach for the numerical solution of a class of nonlocal diffusion problems

M. Gunzburger, C. Webster, G. Zhang, 2015 .

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

R. Tempone, C. Webster, F. Nobile, 2006 .

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

Raul Tempone, Fabio Nobile, Clayton Webster, 2008 .

The analysis of a sparse grid stochastic collocation method for partial differential equations with high-dimensional random input data.

Raul Tempone, Fabio Nobile, Clayton Webster, 2007 .

A surrogate modeling approach for crack pattern prediction in peridynamics

C. Webster, M. Stoyanov, Pablo Seleson, 2017 .

Sparse Collocation Methods for Stochastic Interpolation and Quadrature

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

A multilevel stochastic collocation method for SPDEs

M. Gunzburger, C. Webster, A. Teckentrup, 2015 .

A convergence analysis of stochastic collocation method for Navier-Stokes equations with random input data

C. Webster, C. Trenchea, Hoang Tran, 2014 .

Analysis of sparse recovery for Legendre expansions using envelope bound

C. Webster, Hoang Tran, 2018, Numerical Methods for Partial Differential Equations.

Committees Scientific Committee Organizing Committee Plenary Speakers Schedule Schedule Schedule Xylar Asay-davis Schedule Room: Cbc C116 Mini-symposium 4 Advances in Analytical and Computational Techniques for Nonlinear Waves Organizer: Room: Cbc C118 Mini-symposium 9 Uncertainty Quantification for

C. S. Chen, Zhangxing, Eric A. Machorro, 2022 .

Neural network integral representations with the ReLU activation function

C. Webster, A. Dereventsov, A. Petrosyan, 2019, MSML.

An improved multilevel Monte Carlo method for estimating probability distribution functions in stochastic oil reservoir simulations

C. Webster, Guannan Zhang, D. Lu, 2016 .

Examining Policy Entropy of Reinforcement Learning Agents for Personalization Tasks

C. Webster, A. Dereventsov, Andrew Starnes, 2022, ArXiv.

A class of null space conditions for sparse recovery via nonconvex, non-separable minimizations

C. Webster, Hoang Tran, 2017, Results in Applied Mathematics.

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

C. Webster, Guannan Zhang, Hoang Tran, 2017, Numerische Mathematik.

On the Strong Convergence of Forward-Backward Splitting in Reconstructing Jointly Sparse Signals

C. Webster, Hoang Tran, N. Dexter, 2021, Set-Valued and Variational Analysis.

Numerical solution of backward stochastic differential equations with jumps for a class of nonlocal diffusion problems

M. Gunzburger, C. Webster, W. Zhao, 2015 .

An Improved Discrete Least-Squares/Reduced-Basis Method for Parameterized Elliptic PDEs

M. Gunzburger, C. Webster, Guannan Zhang, 2018, Journal of Scientific Computing.

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

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

An Efficient Meshfreee Implicit Filter for Nonlinear Filtering Problems

Guannan Zhang, Feng Bao, Yanzhao Cao, 2015, 1508.00783.

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.

Applied Mathematics Research for Exascale Computing

Jack Dongarra, Paul D. Hovland, Stefan M. Wild, 2014 .

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

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

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..

Plasma Surface Interactions: Predicting the Performance and Impact of Dynamic PFC Surfaces

M. Shephard, C. Webster, G. Nandipati, 2018 .

Analysis of the ratio of ℓ1 and ℓ2 norms in compressed sensing

C. Webster, Hoang Tran, Yiming Xu, 2021 .

Analysis of The Ratio of $\ell_1$ and $\ell_2$ Norms in Compressed Sensing pdf

Yiming Xu, Hoang Tran, Akil Narayan, 2020, ArXiv.

Reduced basis methods for nonlocal diffusion problems with random input data

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

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

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

LDRD Final Report: Capabilities for Uncertainty in Predictive Science.

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

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..

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 .

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

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

An asymptotically compatible probabilistic collocation method for randomly heterogeneous nonlocal problems pdf

Xiaochuan Tian, Clayton Webster, Yue Yu, 2021, J. Comput. Phys..

Examining Policy Entropy of Reinforcement Learning Agents for Personalization Tasks

C. Webster, A. Dereventsov, Andrew Starnes, 2022, arXiv.org.

On the Unreasonable Efficiency of State Space Clustering in Personalization Tasks

Anton Dereventsov, Clayton Webster, Ranga Raju Vatsavai, 2021, 2021 International Conference on Data Mining Workshops (ICDMW).

Offline Policy Comparison under Limited Historical Agent-Environment Interactions pdf

Anton Dereventsov, Clayton Webster, Joseph D. Daws, 2021, ArXiv.

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

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

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

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

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

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

Compressed Sensing Approaches for Polynomial Approximation of High-Dimensional Functions

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

Foreword

C. Webster, Richard K. Archibald, R. Archibald, 2014, Int. J. Comput. Math..

Methods in Computational Science

C. Webster, S. Brugiapaglia, B. Adcock, 2022 .

Evolve Then Filter Regularization for Stochastic Reduced Order Modeling

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

The Natural Greedy Algorithm for reduced bases in Banach spaces

C. Webster, A. Dereventsov, Anton Dereventsov, 2019, 1905.06448.

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

John Shalf, Jack Dongarra, Vivek Sarkar, 2014 .

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..

THE LOSS OF ACCURACY OF STOCHASTIC COLLOCATION METHOD IN SOLVING NONLINEAR DIFFERENTIAL EQUATIONS WITH RANDOM INPUT DATA

C. Webster, C. Trenchea, Hoang Tran, 2013 .

Non-intrusive inference reduced order model for fluids using linear multistep neural network

C. Webster, Guannan Zhang, X. Xie, 2018, 1809.07820.

Robust learning with implicit residual networks pdf

Viktor Reshniak, Clayton Webster, C. Webster, 2019, Mach. Learn. Knowl. Extr..

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.

Analysis of Deep Neural Networks with Quasi-optimal polynomial approximation rates pdf

Joseph Daws, Clayton Webster, Joseph D. Daws, 2019, ArXiv.

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

R. Tempone, C. Webster, F. Nobile, 2008, SIAM J. Numer. Anal..

Closure Learning for Nonlinear Model Reduction Using Deep Residual Neural Network

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

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.

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

R. Tempone, C. Webster, F. Nobile, 2008, SIAM J. Numer. Anal..

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.

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

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

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

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

Reconstructing high-dimensional Hilbert-valued functions via compressed sensing

Nick Dexter, Hoang Tran, Clayton Webster, 2019, 2019 13th International conference on Sampling Theory and Applications (SampTA).

Polynomial approximation of high-dimensional functions via compressed sensing

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

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

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

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..

Greedy Shallow Networks: A New Approach for Constructing and Training Neural Networks pdf

Anton Dereventsov, Clayton Webster, Armenak Petrosyan, 2019, ArXiv.

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.

Analytic Continuation of Noisy Data Using Adams Bashforth ResNet

C. Webster, T. Maier, F. Bao, 2019, 1905.10430.

Reconstruction of jointly sparse vectors via manifold optimization pdf

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

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

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

Hyperspherical Sparse Approximation Techniques for High-Dimensional Discontinuity Detection

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

High Performance Computing Simulations for Shale Gas Formation : Flow Transport and Uncertainty Quantification Analysis

Franklin G. Curtis, C. Webster, N. Collier, 2015 .