John D. Jakeman
Orcid: 0000-0002-3517-337X
According to our database1,
John D. Jakeman
authored at least 34 papers
between 2006 and 2024.
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Bibliography
2024
2023
PyApprox: A software package for sensitivity analysis, Bayesian inference, optimal experimental design, and multi-fidelity uncertainty quantification and surrogate modeling.
Environ. Model. Softw., December, 2023
Epistemic Uncertainty-Aware Barlow Twins Reduced Order Modeling for Nonlinear Contact Problems.
IEEE Access, 2023
2022
Surrogate modeling for efficiently, accurately and conservatively estimating measures of risk.
Reliab. Eng. Syst. Saf., 2022
SIAM/ASA J. Uncertain. Quantification, 2022
Reverse-mode differentiation in arbitrary tensor network format: with application to supervised learning.
J. Mach. Learn. Res., 2022
Assessing the predictive impact of factor fixing with an adaptive uncertainty-based approach.
Environ. Model. Softw., 2022
2021
The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support.
Environ. Model. Softw., 2021
2020
A generalized approximate control variate framework for multifidelity uncertainty quantification.
J. Comput. Phys., 2020
Optimal experimental design for prediction based on push-forward probability measures.
J. Comput. Phys., 2020
CoRR, 2020
A Survey of Constrained Gaussian Process Regression: Approaches and Implementation Challenges.
CoRR, 2020
2019
Introductory overview of identifiability analysis: A guide to evaluating whether you have the right type of data for your modeling purpose.
Environ. Model. Softw., 2019
A neural network approach for uncertainty quantification for time-dependent problems with random parameters.
CoRR, 2019
Adaptive Multi-index Collocation for Uncertainty Quantification and Sensitivity Analysis.
CoRR, 2019
2018
Convergence of Probability Densities Using Approximate Models for Forward and Inverse Problems in Uncertainty Quantification.
SIAM J. Sci. Comput., 2018
Combining Push-Forward Measures and Bayes' Rule to Construct Consistent Solutions to Stochastic Inverse Problems.
SIAM J. Sci. Comput., 2018
Compressed Sensing with Sparse Corruptions: Fault-Tolerant Sparse Collocation Approximations.
SIAM/ASA J. Uncertain. Quantification, 2018
J. Comput. Phys., 2018
2017
A Generalized Sampling and Preconditioning Scheme for Sparse Approximation of Polynomial Chaos Expansions.
SIAM J. Sci. Comput., 2017
A Christoffel function weighted least squares algorithm for collocation approximations.
Math. Comput., 2017
2015
Local Polynomial Chaos Expansion for Linear Differential Equations with High Dimensional Random Inputs.
SIAM J. Sci. Comput., 2015
Enhancing adaptive sparse grid approximations and improving refinement strategies using adjoint-based a posteriori error estimates.
J. Comput. Phys., 2015
Enhancing ℓ1-minimization estimates of polynomial chaos expansions using basis selection.
J. Comput. Phys., 2015
2014
Adaptive Leja Sparse Grid Constructions for Stochastic Collocation and High-Dimensional Approximation.
SIAM J. Sci. Comput., 2014
Enhancing ℓ<sub>1</sub>-minimization estimates of polynomial chaos expansions using basis selection.
CoRR, 2014
2013
Minimal multi-element stochastic collocation for uncertainty quantification of discontinuous functions.
J. Comput. Phys., 2013
2011
Characterization of discontinuities in high-dimensional stochastic problems on adaptive sparse grids.
J. Comput. Phys., 2011
2010
J. Comput. Phys., 2010
2006