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ORCIDJohn D. Jakeman
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2020 – today
- 2024
[i11]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 abs/2402.13768 (2024)- [i10]Alex A. Gorodetsky, John D. Jakeman, Michael S. Eldred:
Grouped approximate control variate estimators. CoRR abs/2402.14736 (2024) - [i9]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 abs/2407.00809 (2024) - 2023
- [j24]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: 62970-62985 (2023) - [j23]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: 105825 (2023) - 2022
- [j22]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: 105290 (2022) - [j21]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: 143:1-143:29 (2022) - [j20]Drew P. Kouri, John D. Jakeman, J. Gabriel Huerta:
Risk-Adapted Optimal Experimental Design. SIAM/ASA J. Uncertain. Quantification 10(2): 687-716 (2022) - [j19]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: 108280 (2022) - 2021
- [j18]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: 104954 (2021) - [j17]Tong Qin, Zhen Chen, John D. Jakeman, Dongbin Xiu:
Data-Driven Learning of Nonautonomous Systems. SIAM J. Sci. Comput. 43(3): A1607-A1624 (2021) - 2020
- [j16]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: 109257 (2020) - [j15]Troy D. Butler, John D. Jakeman, Tim Wildey:
Optimal experimental design for prediction based on push-forward probability measures. J. Comput. Phys. 416: 109518 (2020) - [i8]Tong Qin, Zhen Chen, John D. Jakeman, Dongbin Xiu:
Data-driven learning of non-autonomous systems. CoRR abs/2006.02392 (2020) - [i7]Laura P. Swiler, Mamikon A. Gulian, Ari Frankel, Cosmin Safta, John D. Jakeman:
A Survey of Constrained Gaussian Process Regression: Approaches and Implementation Challenges. CoRR abs/2006.09319 (2020) - [i6]Alex A. Gorodetsky, John D. Jakeman, Gianluca Geraci:
MFNets: Learning network representations for multifidelity surrogate modeling. CoRR abs/2008.02672 (2020)
2010 – 2019
- 2019
- [j14]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: 418-432 (2019) - [i5]John D. Jakeman, Michael Eldred, Gianluca Geraci, Alex A. Gorodetsky:
Adaptive Multi-index Collocation for Uncertainty Quantification and Sensitivity Analysis. CoRR abs/1909.13845 (2019) - [i4]Tong Qin, Zhen Chen, John D. Jakeman, Dongbin Xiu:
A neural network approach for uncertainty quantification for time-dependent problems with random parameters. CoRR abs/1910.07096 (2019) - 2018
- [j13]Alex A. Gorodetsky, John D. Jakeman:
Gradient-based optimization for regression in the functional tensor-train format. J. Comput. Phys. 374: 1219-1238 (2018) - [j12]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): 1424-1453 (2018) - [j11]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) - [j10]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): A3523-A3548 (2018) - 2017
- [j9]Akil Narayan, John D. Jakeman, Tao Zhou:
A Christoffel function weighted least squares algorithm for collocation approximations. Math. Comput. 86(306): 1913-1947 (2017) - [j8]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) - 2015
- [j7]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: 54-71 (2015) - [j6]John D. Jakeman, Michael S. Eldred, Khachik Sargsyan:
Enhancing ℓ1-minimization estimates of polynomial chaos expansions using basis selection. J. Comput. Phys. 289: 18-34 (2015) - [j5]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) - 2014
- [j4]Akil Narayan, John D. Jakeman:
Adaptive Leja Sparse Grid Constructions for Stochastic Collocation and High-Dimensional Approximation. SIAM J. Sci. Comput. 36(6) (2014) - [i3]John D. Jakeman, Timothy Wildey:
Enhancing adaptive sparse grid approximations and improving refinement strategies using adjoint-based a posteriori error estimates. CoRR abs/1407.1061 (2014) - [i2]John D. Jakeman, Michael S. Eldred, Khachik Sargsyan:
Enhancing ℓ1-minimization estimates of polynomial chaos expansions using basis selection. CoRR abs/1407.8093 (2014) - 2013
- [j3]John D. Jakeman, Akil Narayan, Dongbin Xiu:
Minimal multi-element stochastic collocation for uncertainty quantification of discontinuous functions. J. Comput. Phys. 242: 790-808 (2013) - 2011
- [j2]John D. Jakeman, Richard Archibald, Dongbin Xiu:
Characterization of discontinuities in high-dimensional stochastic problems on adaptive sparse grids. J. Comput. Phys. 230(10): 3977-3997 (2011) - [i1]John D. Jakeman, Stephen G. Roberts:
Local and Dimension Adaptive Sparse Grid Interpolation and Quadrature. CoRR abs/1110.0010 (2011) - 2010
- [j1]John D. Jakeman, Michael S. Eldred, Dongbin Xiu:
Numerical approach for quantification of epistemic uncertainty. J. Comput. Phys. 229(12): 4648-4663 (2010)
2000 – 2009
- 2006
- [c1]Stephen Gwyn Roberts, Ole Møller Nielsen, John D. Jakeman:
Simulation of Tsunami and Flash Floods. HPSC 2006: 489-498
Coauthor Index
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