J. Jakeman

发表

Randomized Algorithms for Scientific Computing (RASC) pdf

Mihai Anitescu, Stephen J. Wright, Draguna Vrabie, 2021, ArXiv.

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

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

Modeling Water Quality in Watersheds: From Here to the Next Generation

J. Jakeman, V. Snow, B. Croke, 2020, Water resources research.

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.

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 .

A Consistent Bayesian Formulation for Stochastic Inverse Problems Based on Push-forward Measures

J. D. Jakeman, J. Jakeman, T. Wildey, 2017, 1704.00680.

Adaptive experimental design for multi‐fidelity surrogate modeling of multi‐disciplinary systems

M. Eldred, J. Jakeman, M. Eldred, 2022, International Journal for Numerical Methods in Engineering.

Polynomial chaos expansions for dependent random variables

M. Eldred, J. Jakeman, A. Narayan, 2019, Computer Methods in Applied Mechanics and Engineering.

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

M. Eldred, J. Jakeman, G. Geraci, 2019 .

Risk-Adapted Optimal Experimental Design

J. Jakeman, D. Kouri, J. Gabriel Huerta, 2022, SIAM/ASA J. Uncertain. Quantification.

Dakota A Multilevel Parallel Object-Oriented Framework for Design Optimization Parameter Estimation Uncertainty Quantification and Sensitivity Analysis: Version 6.12 Theory Manual.

Jason A. Monschke, M. Eldred, J. Jakeman, 2020 .

Quantification of Uncertainty in Extreme Scale Computations (QUEST).

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

Inundation Modelling of the December 2004 Indian Ocean Tsunami

J. Jakeman, S. Roberts, O. Nielsen, 2007 .

Global Sensitivity Analysis Using the Ultra‐Low Resolution Energy Exascale Earth System Model

J. Jakeman, Amy Powell, I. Tezaur, 2021, Journal of Advances in Modeling Earth Systems.

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..

TIME AND FREQUENCY DOMAIN METHODS FOR BASIS SELECTION IN RANDOM LINEAR DYNAMICAL SYSTEMS

J. Jakeman, R. Pulch, 2018, 1809.00434.

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

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

Numerical methods for the quantification of uncertainty in discontinuous functions of high dimension

J. Jakeman, 2011 .

Computational Methods in Ice Sheet Modeling for Next-Generation Climate Simulations

R. Tuminaro, M. Eldred, J. Jakeman, 2015 .

Non-destructive simulation of node defects in additively manufactured lattice structures

J. Jakeman, M. Brandt, A. du Plessis, 2020 .

LDRD PROJECT TITLE : Adaptive Bayesian Inference for Prediction PROJECT TEAM MEMBERS :

J. Jakeman, T. Wildey, 2015 .

DEEP LEARNING OF PARAMETERIZED EQUATIONS WITH APPLICATIONS TO UNCERTAINTY QUANTIFICATION

J. Jakeman, D. Xiu, Tong Qin, 2019, International Journal for Uncertainty Quantification.

Surrogate modeling for efficiently, accurately and conservatively estimating measures of risk

J. Jakeman, D. Kouri, Jose Huerta, 2021, Reliab. Eng. Syst. Saf..

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..

Practical identifiability analysis of environmental models.

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

A Survey of Constrained Gaussian Process Regression: Approaches and Implementation Challenges pdf

Mamikon Gulian, John Jakeman, Laura Swiler, 2020, ArXiv.

Aluminium and dental materials--a study in vitro of its potential release and toxicity.

J. Jakeman, S. Meryon, 1987, International Endodontic Journal.

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..

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..

A generalized approximate control variate framework for multifidelity uncertainty quantification

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

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.

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

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

Optimal Experimental Design Using A Consistent Bayesian Approach

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

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 .

Dakota A Multilevel Parallel Object-Oriented Framework for Design Optimization Parameter Estimation Uncertainty Quantification and Sensitivity Analysis: Version 6.12 User?s Manual.

Mohamed S. Ebeida, Kenneth T. Hu, Jason A. Monschke, 2020 .

Treating Computer Experiment: What Matters What Doesn't What Evidence.

John Davis Jakeman, Jerome Sacks, Jason L. Loeppky, 2014 .

Randomized Algorithms for Scientific Computing (RASC)

Stephen J. Wright, Stefan M. Wild, Miles E. Lopes, 2021, ArXiv.

Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.13 User's Manual.

Mohamed S. Ebeida, Jason A. Monschke, B. Adams, 2020 .

An Overview of Methods to Identify and Manage Uncertainty for Modelling Problems in the Water–Environment–Agriculture Cross-Sector

A. J. Jakeman, J. D. Jakeman, J. Jakeman, 2016 .

MFNets: MULTI-FIDELITY DATA-DRIVEN NETWORKS FOR BAYESIAN LEARNING AND PREDICTION

M. Eldred, J. Jakeman, G. Geraci, 2020, International Journal for Uncertainty Quantification.

Towards spatially distributed quantitative assessment of tsunami inundation models

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

Numerical approach for quantification of epistemic uncertainty

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

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..

Recent Advances in Adaptive Refinement of (Regression-Based) Multifidelity Surrogates for UQ.

M. Eldred, J. Jakeman, G. Geraci, 2021, Proposed for presentation at the SIAM CSE21 held March 1-5, 2021 in Fort Worth, TX..

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.

Uncertainty Quantification

I. Tezaur, A. Salinger, M. Perego, 2011, Encyclopedia of Parallel Computing.

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

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

Epistemic Uncertainty-Aware Barlow Twins Reduced Order Modeling for Nonlinear Contact Problems

J. Jakeman, Youngsoo Choi, N. Bouklas, 2022, IEEE Access.

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..

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

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

Dakota A Multilevel Parallel Object-Oriented Framework for Design Optimization Parameter Estimation Uncertainty Quantification and Sensitivity Analysis: Version 6.14 Theory Manual.

Jason A. Monschke, M. Eldred, J. Jakeman, 2022 .

A Christoffel function weighted least squares algorithm for collocation approximations

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

Global sensitivity analysis using the ultra-low 1 resolution Energy Exascale Earth System Model 2

J. Jakeman, Amy Powell, I. Tezaur, 2021 .

Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.16 User's Manual.

Mohamed S. Ebeida, Jason A. Monschke, B. Adams, 2022 .

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

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

Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.15 Theory Manual.

Jason A. Monschke, M. Eldred, J. Jakeman, 2021 .

PyApprox: Enabling efficient model analysis.

J. Jakeman, 2022 .

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

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

A Decision‐Relevant Factor‐Fixing Framework: Application to Uncertainty Analysis of a High‐Dimensional Water Quality Model

J. Jakeman, B. Croke, A. Jakeman, 2023, Water Resources Research.

Dakota A Multilevel Parallel Object-Oriented Framework for Design Optimization Parameter Estimation Uncertainty Quantification and Sensitivity Analysis: Version 6.14 User's Manual.

B. Adams, P. Hough, M. Eldred, 2020 .

Dakota A Multilevel Parallel Object-Oriented Framework for Design Optimization Parameter Estimation Uncertainty Quantification and Sensitivity Analysis: Version 6.14 Theory Manual.

Mohamed S. Ebeida, Dena M. Vigil, P. Hough, 2020 .

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..

Strategies for Automation of Model Tuning in Multi-fidelity Trajectory Uncertainty Propagation

M. Eldred, J. Jakeman, G. Geraci, 2023, AIAA SCITECH 2023 Forum.

Improving Multi-Model Trajectory Simulation Estimators using Model Selection and Tuning

M. Eldred, J. Jakeman, G. Geraci, 2022, AIAA SCITECH 2022 Forum.

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

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

Hyper-differential sensitivity analysis in the context of Bayesian inference applied to ice-sheet problems

B. V. B. Waanders, J. Jakeman, A. Saibaba, 2022, International Journal for Uncertainty Quantification.

Dakota A Multilevel Parallel Object-Oriented Framework for Design Optimization Parameter Estimation Uncertainty Quantification and Sensitivity Analysis: Version 6.14 User's Manual.

Mohamed S. Ebeida, B. Adams, P. Hough, 2020 .

Cholesky-Based Experimental Design for Gaussian Process and Kernel-Based Emulation and Calibration

Helumt Harbrecht, J. Jakeman, H. Harbrecht, 2021, Communications in Computational Physics.

Beauty and the beast: Some perspectives on efficient model analysis, surrogate models, and the future of modeling

B. Tolson, M. C. Hill, J. Jakeman, 2015 .

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..

Generation and application of multivariate polynomial quadrature rules

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

Grouped approximate control variate estimators

M. Eldred, J. Jakeman, A. Gorodetsky, 2024, ArXiv.

Multifidelity uncertainty quantification with models based on dissimilar parameters

M. Eldred, J. Jakeman, R. Ghanem, 2023, Computer Methods in Applied Mechanics and Engineering.

Improving Bayesian networks multifidelity surrogate construction with basis adaptation

M. Eldred, J. Jakeman, R. Ghanem, 2023, AIAA SCITECH 2023 Forum.

MFNets: Learning network representations for multifidelity surrogate modeling pdf

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

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

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

Data-driven learning of non-autonomous systems pdf

Dongbin Xiu, Zhen Chen, Tong Qin, 2020, SIAM J. Sci. Comput..

DEEP LEARNING OF PARAMETERIZED EQUATIONS WITH APPLICATIONS TO UNCERTAINTY QUANTIFICATION

J. Jakeman, D. Xiu, Tong Qin, 2019, International Journal for Uncertainty Quantification.