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John 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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last updated on 2024-08-11 01:07 CEST by the dblp team
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