John D. Jakeman

Publication Activity (10 Years)

Years Active: 2006-2024
Publications (10 Years): 28

Top Topics

Inverse Problems

Experimental Designs

Probability Measure

Top Venues

SIAM J. Sci. Comput.

J. Comput. Phys.

Environ. Model. Softw.

Publications

Linus Seelinger, Anne Reinarz, Mikkel Bue Lykkegaard, Amal Mohammed A. Alghamdi, David Aristoff, Wolfgang Bangerth, Jean Bénézech, Matteo Diez, Kurt Frey, John D. Jakeman, Jakob Sauer Jørgensen, Ki-Tae Kim, Massimiliano Martinelli, Matthew D. Parno, Riccardo Pellegrini, Noemi Petra, Nicolai André Brogaard Riis, Katherine Rosenfeld, Andrea Serani, Lorenzo Tamellini, Umberto Villa, Tim J. Dodwell, Robert Scheichl
Democratizing Uncertainty Quantification. CoRR (2024)
Alex A. Gorodetsky, John D. Jakeman, Michael S. Eldred
Grouped approximate control variate estimators. CoRR (2024)
Matthew Lowery, John Turnage, Zachary Morrow, John D. Jakeman, Akil Narayan, Shandian Zhe, Varun Shankar
Kernel Neural Operators (KNOs) for Scalable, Memory-efficient, Geometrically-flexible Operator Learning. CoRR (2024)
John D. Jakeman
PyApprox: A software package for sensitivity analysis, Bayesian inference, optimal experimental design, and multi-fidelity uncertainty quantification and surrogate modeling. Environ. Model. Softw. 170 (2023)
Teeratorn Kadeethum, John D. Jakeman, Youngsoo Choi, Nikolaos Bouklas, Hongkyu Yoon
Epistemic Uncertainty-Aware Barlow Twins Reduced Order Modeling for Nonlinear Contact Problems. IEEE Access 11 (2023)
Alex A. Gorodetsky, Cosmin Safta, John D. Jakeman
Reverse-mode differentiation in arbitrary tensor network format: with application to supervised learning. J. Mach. Learn. Res. 23 (2022)
John D. Jakeman, Drew P. Kouri, J. Gabriel Huerta
Surrogate modeling for efficiently, accurately and conservatively estimating measures of risk. Reliab. Eng. Syst. Saf. 221 (2022)
Drew P. Kouri, John D. Jakeman, J. Gabriel Huerta
Risk-Adapted Optimal Experimental Design. SIAM/ASA J. Uncertain. Quantification 10 (2) (2022)
Qian Wang, Joseph H. A. Guillaume, John D. Jakeman, Tao Yang, Takuya Iwanaga, Barry F. W. Croke, Anthony J. Jakeman
Assessing the predictive impact of factor fixing with an adaptive uncertainty-based approach. Environ. Model. Softw. 148 (2022)
Saman Razavi, Anthony J. Jakeman, Andrea Saltelli, Clémentine Prieur, Bertrand Iooss, Emanuele Borgonovo, Elmar Plischke, Samuele Lo Piano, Takuya Iwanaga, William E. Becker, Stefano Tarantola, Joseph H. A. Guillaume, John D. Jakeman, Hoshin V. Gupta, Nicola Melillo, Giovanni Rabitti, Vincent Chabridon, Qingyun Duan, Holger R. Maier
The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support. Environ. Model. Softw. 137 (2021)
Tong Qin, Zhen Chen, John D. Jakeman, Dongbin Xiu
Data-Driven Learning of Nonautonomous Systems. SIAM J. Sci. Comput. 43 (3) (2021)
Alex A. Gorodetsky, John D. Jakeman, Gianluca Geraci
MFNets: Learning network representations for multifidelity surrogate modeling. CoRR (2020)
Troy D. Butler, John D. Jakeman, Tim Wildey
Optimal experimental design for prediction based on push-forward probability measures. J. Comput. Phys. 416 (2020)
Laura P. Swiler, Mamikon Gulian, Ari Frankel, Cosmin Safta, John D. Jakeman
A Survey of Constrained Gaussian Process Regression: Approaches and Implementation Challenges. CoRR (2020)
Tong Qin, Zhen Chen, John D. Jakeman, Dongbin Xiu
Data-driven learning of non-autonomous systems. CoRR (2020)
Alex A. Gorodetsky, Gianluca Geraci, Michael S. Eldred, John D. Jakeman
A generalized approximate control variate framework for multifidelity uncertainty quantification. J. Comput. Phys. 408 (2020)
John D. Jakeman, Michael Eldred, Gianluca Geraci, Alex A. Gorodetsky
Adaptive Multi-index Collocation for Uncertainty Quantification and Sensitivity Analysis. CoRR (2019)
Joseph H. A. Guillaume, John D. Jakeman, Stefano Marsili-Libelli, Michael Asher, Philip Brunner, Barry F. W. Croke, Mary C. Hill, Anthony J. Jakeman, Karel J. Keesman, Saman Razavi, Johannes D. Stigter
Introductory overview of identifiability analysis: A guide to evaluating whether you have the right type of data for your modeling purpose. Environ. Model. Softw. 119 (2019)
Tong Qin, Zhen Chen, John D. Jakeman, Dongbin Xiu
A neural network approach for uncertainty quantification for time-dependent problems with random parameters. CoRR (2019)
Alex A. Gorodetsky, John D. Jakeman
Gradient-based optimization for regression in the functional tensor-train format. J. Comput. Phys. 374 (2018)
Troy D. Butler, John D. Jakeman, Tim Wildey
Convergence of Probability Densities Using Approximate Models for Forward and Inverse Problems in Uncertainty Quantification. SIAM J. Sci. Comput. 40 (5) (2018)
Ben Adcock, Anyi Bao, John D. Jakeman, Akil Narayan
Compressed Sensing with Sparse Corruptions: Fault-Tolerant Sparse Collocation Approximations. SIAM/ASA J. Uncertain. Quantification 6 (4) (2018)
Troy D. Butler, John D. Jakeman, Tim Wildey
Combining Push-Forward Measures and Bayes' Rule to Construct Consistent Solutions to Stochastic Inverse Problems. SIAM J. Sci. Comput. 40 (2) (2018)
Akil Narayan, John D. Jakeman, Tao Zhou
A Christoffel function weighted least squares algorithm for collocation approximations. Math. Comput. 86 (306) (2017)
John D. Jakeman, Akil Narayan, Tao Zhou
A Generalized Sampling and Preconditioning Scheme for Sparse Approximation of Polynomial Chaos Expansions. SIAM J. Sci. Comput. 39 (3) (2017)
Yi Chen, John D. Jakeman, Claude Jeffrey Gittelson, Dongbin Xiu
Local Polynomial Chaos Expansion for Linear Differential Equations with High Dimensional Random Inputs. SIAM J. Sci. Comput. 37 (1) (2015)
John D. Jakeman, Michael S. Eldred, Khachik Sargsyan
Enhancing ℓ1-minimization estimates of polynomial chaos expansions using basis selection. J. Comput. Phys. 289 (2015)
John D. Jakeman, Timothy Wildey
Enhancing adaptive sparse grid approximations and improving refinement strategies using adjoint-based a posteriori error estimates. J. Comput. Phys. 280 (2015)
Akil Narayan, John D. Jakeman
Adaptive Leja Sparse Grid Constructions for Stochastic Collocation and High-Dimensional Approximation. SIAM J. Sci. Comput. 36 (6) (2014)
John D. Jakeman, Michael S. Eldred, Khachik Sargsyan
-minimization estimates of polynomial chaos expansions using basis selection. CoRR (2014)
John D. Jakeman, Timothy Wildey
Enhancing adaptive sparse grid approximations and improving refinement strategies using adjoint-based a posteriori error estimates. CoRR (2014)
John D. Jakeman, Akil Narayan, Dongbin Xiu
Minimal multi-element stochastic collocation for uncertainty quantification of discontinuous functions. J. Comput. Phys. 242 (2013)
John D. Jakeman, Richard Archibald, Dongbin Xiu
Characterization of discontinuities in high-dimensional stochastic problems on adaptive sparse grids. J. Comput. Phys. 230 (10) (2011)
John D. Jakeman, Stephen G. Roberts
Local and Dimension Adaptive Sparse Grid Interpolation and Quadrature CoRR (2011)
John D. Jakeman, Michael S. Eldred, Dongbin Xiu
Numerical approach for quantification of epistemic uncertainty. J. Comput. Phys. 229 (12) (2010)
Stephen Gwyn Roberts, Ole Møller Nielsen, John D. Jakeman
Simulation of Tsunami and Flash Floods. HPSC (2006)