John D. Jakeman

发表

A Posteriori Error Analysis and Adaptive Construction of Surrogate Models.

Clint Dawson, Paul G. Constantine, Serge Prudhomme, 2013 .

Adaptive Bayesian Inference for Prediction

Timothy Michael Wildey, John D. Jakeman, 2015 .

MFNets: Learning network representations for multifidelity surrogate modeling pdf

Gianluca Geraci, John D. Jakeman, Alex Gorodetsky, 2020, ArXiv.

Minimal multi-element stochastic collocation for uncertainty quantification of discontinuous functions

Dongbin Xiu, Akil C. Narayan, John D. Jakeman, 2013, J. Comput. Phys..

Simulation of Tsunami and Flash Floods

Stephen G. Roberts, Ole Møller Nielsen, John D. Jakeman, 2006, HPSC.

A Generalized Sampling and Preconditioning Scheme for Sparse Approximation of Polynomial Chaos Expansions

Akil C. Narayan, John D. Jakeman, Tao Zhou, 2016, SIAM J. Sci. Comput..

Local and Dimension Adaptive Stochastic Collocation for Uncertainty Quantification

Stephen Roberts, John D. Jakeman, 2012 .

Enhancing ℓ1-minimization estimates of polynomial chaos expansions using basis selection

Khachik Sargsyan, John D. Jakeman, Michael Eldred, 2014, J. Comput. Phys..

Albany/FELIX: A Robust & Scalable Trilinos- Based Finite-Element Ice Flow Dycore Built for Advanced Architectures & Analysis

Michael S. Eldred, Andrew G. Salinger, Raymond S. Tuminaro, 2015 .

Adaptive sparse grids for uncertainty quantication Enhancing approximations using a posteriori error estimation.

Michael S. Eldred, Tim Wildey, John D. Jakeman, 2012 .

Enhancing adaptive sparse grid approximations and improving refinement strategies using adjoint-based a posteriori error estimates

John D. Jakeman, Timothy Wildey, J. Jakeman, 2014, J. Comput. Phys..

Neural Networks as Surrogates of Nonlinear High-Dimensional Parameter-to-Prediction Maps.

William M. Severa, Mauro Perego, William Severa, 2018 .

Combining Push-Forward Measures and Bayes' Rule to Construct Consistent Solutions to Stochastic Inverse Problems

Tim Wildey, Troy D. Butler, John D. Jakeman, 2018, SIAM J. Sci. Comput..

Quantification of Uncertainty in Extreme Scale Computations (QUEST).

Cosmin Safta, Michael S. Eldred, Habib N. Najm, 2012 .

Scalable Environment for Quantification of Uncertainty and Optimization in Industrial Applications (SEQUOIA)

Charbel Farhat, Gianluca Iaccarino, Paul G. Constantine, 2017 .

Local and Dimension Adaptive Sparse Grid Interpolation and Quadrature pdf

Stephen G. Roberts, John D. Jakeman, 2011, ArXiv.

Introductory overview of identifiability analysis: A guide to evaluating whether you have the right type of data for your modeling purpose

Joseph H. A. Guillaume, Anthony J. Jakeman, Stefano Marsili-Libelli, 2019, Environ. Model. Softw..

Numerical approach for quantification of epistemic uncertainty

Dongbin Xiu, John D. Jakeman, Michael Eldred, 2010, J. Comput. Phys..

LDRD Project Summary: Incorporating physical constraints into Gaussian process surrogate models.

Laura Painton Swiler, Mamikon Gulian, John D. Jakeman, 2020 .

The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support

Emanuele Borgonovo, Joseph H. A. Guillaume, Anthony J. Jakeman, 2020, Environ. Model. Softw..

Adaptive multi‐index collocation for uncertainty quantification and sensitivity analysis pdf

Gianluca Geraci, John D. Jakeman, Alex Gorodetsky, 2019, International Journal for Numerical Methods in Engineering.

Dakota, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis version 6.0 theory manual

Mohamed S. Ebeida, Michael S. Eldred, William J. Bohnhoff, 2020 .

Compressed Sensing with Sparse Corruptions: Fault-Tolerant Sparse Collocation Approximations

Ben Adcock, Akil Narayan, John D. Jakeman, 2017, SIAM/ASA J. Uncertain. Quantification.

Quantifying Uncertainty using a-posteriori Enhanced Sparse Grid Approximations.

Tim Wildey, John D. Jakeman, 2013 .

Practical identifiability analysis of environmental models.

Joseph H. A. Guillaume, Stefano Marsili-Libelli, M. B. Beck, 2014 .

Local Polynomial Chaos Expansion for Linear Differential Equations with High Dimensional Random Inputs

Dongbin Xiu, Yi Chen, Claude Jeffrey Gittelson, 2015, SIAM J. Sci. Comput..

Correction: Multilevel-Multidelity Approaches for Forward UQ in the DARPA SEQUOIA project

Gianluca Geraci, Michael S. Eldred, Alex A. Gorodetsky, 2018 .

Extending adaptive sparse grids for stochastic collocation to hybrid parallel architectures

Ralf Deiterding, Rick Archibald, John D. Jakeman, 2012 .

Generation and application of multivariate polynomial quadrature rules

Akil Narayan, John D. Jakeman, J. Jakeman, 2017, Computer Methods in Applied Mechanics and Engineering.

Optimal Experimental Design Using A Consistent Bayesian Approach

John D. Jakeman, Scott N. Walsh, Tim M. Wildey, 2017, 1705.09395.

Adjoint-Based a Posteriori Error Estimation and Uncertainty Quantification for Shock-Hydrodynamic Applications.

John N. Shadid, Troy Butler, Eric C Cyr, 2015 .

Sandia Software Enabling Extreme-Scale Uncertainty Quantification.

Cosmin Safta, Khachik Sargsyan, Michael S. Eldred, 2015 .

A generalized approximate control variate framework for multifidelity uncertainty quantification

Gianluca Geraci, Alex A. Gorodetsky, John D. Jakeman, 2018, J. Comput. Phys..

Multilevel-Multidelity Approaches for Forward UQ in the DARPA SEQUOIA project

Gianluca Geraci, Michael S. Eldred, Alex A. Gorodetsky, 2018 .

A Survey of Constrained Gaussian Process: Approaches and Implementation Challenges

Laura Painton Swiler, Mamikon Gulian, John D. Jakeman, 2020 .

Characterization of discontinuities in high-dimensional stochastic problems on adaptive sparse grids

Dongbin Xiu, Rick Archibald, John D. Jakeman, 2011, J. Comput. Phys..

Adaptive Leja Sparse Grid Constructions for Stochastic Collocation and High-Dimensional Approximation

Akil C. Narayan, John D. Jakeman, J. Jakeman, 2014, SIAM J. Sci. Comput..

Convergence of Probability Densities Using Approximate Models for Forward and Inverse Problems in Uncertainty Quantification

Tim Wildey, Troy D. Butler, John D. Jakeman, 2018, SIAM J. Sci. Comput..

A Christoffel function weighted least squares algorithm for collocation approximations

Tao Zhou, Akil C. Narayan, John D. Jakeman, 2014, Math. Comput..

Optimal experimental design for prediction based on push-forward probability measures

Tim Wildey, Troy Butler, John D. Jakeman, 2020, J. Comput. Phys..

Correction: Recent advancements in Multilevel-Multifidelity techniques for forward UQ in the DARPA Sequoia project

Gianluca Geraci, Michael S. Eldred, Alex A. Gorodetsky, 2019 .

LDRD #218317: Learning Hidden Structure in Multi-Fidelity Information Sources for Efficient Uncertainty Quantification.

Gianluca Geraci, Michael S. Eldred, Alex A. Gorodetsky, 2020 .

Gradient-based optimization for regression in the functional tensor-train format

Alex A. Gorodetsky, John D. Jakeman, J. Jakeman, 2018, J. Comput. Phys..

Data-Driven Learning of Nonautonomous Systems

Dongbin Xiu, John D. Jakeman, Tong Qin, 2021, SIAM J. Sci. Comput..

MFNets: data efficient all-at-once learning of multifidelity surrogates as directed networks of information sources

Gianluca Geraci, John D. Jakeman, Alex Gorodetsky, 2020, Computational Mechanics.

A Generalized Framework for Approximate Control Variates

Gianluca Geraci, Alex A. Gorodetsky, John D. Jakeman, 2018 .

Adaptive Multi-index Collocation for Uncertainty Quantification and Sensitivity Analysis pdf

Gianluca Geraci, John D. Jakeman, Alex Gorodetsky, 2019, International Journal for Numerical Methods in Engineering.

Approximation and error estimation in high dimensional space for stochastic collocation methods on arbitrary sparse samples

Ralf Deiterding, Rick Archibald, Cory D. Hauck, 2012 .

Towards spatially distributed quantitative assessment of tsunami inundation models

John D. Jakeman, David Burbidge, Nick Horspool, 2010 .

Stochastic galerkin and collocation methods for quantifying uncertainty in differential equations: a review

Stephen Roberts, John D. Jakeman, J. Jakeman, 2009 .