John D. Jakeman

42 papers Misc 1Journal 41

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	An Optimal Weighted Least-Squares Method for Operator Learning. CoRR John Turnage, Matthew Lowery, John D. Jakeman, Zachary Morrow, Akil Narayan, Varun Shankar
2025	J	`jnl`	Building Population-Informed Priors for Bayesian Inference Using Data-Consistent Stochastic Inversion. SIAM/ASA J. Uncertain. Quantification Rebekah D. White, John D. Jakeman, Tim Wildey, Troy D. Butler
2025	J	`jnl`	Democratizing uncertainty quantification. J. Comput. Phys. Linus Seelinger, Anne Reinarz, Mikkel Bue Lykkegaard, Robert Akers, Amal Mohammed A. Alghamdi, David Aristoff, Wolfgang Bangerth, Jean Bénézech, Matteo Diez, Kurt Frey, John D. Jakeman, Jakob Sauer Jørgensen, Ki-Tae Kim, Benjamin M. Kent, 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
2025	J	`jnl`	SUPN: Shallow Universal Polynomial Networks. CoRR Zachary Morrow, Michael Penwarden, Brian Chen, Aurya Javeed, Akil Narayan, John D. Jakeman
2025	J	`jnl`	Verification and Validation for Trustworthy Scientific Machine Learning. CoRR John D. Jakeman, Lorena A. Barba, Joaquim R. R. A. Martins, Thomas O'Leary-Roseberry
2024	J	`jnl`	A switching Kalman filter approach to online mitigation and correction of sensor corruption for inertial navigation. CoRR Artem Mustaev, Nicholas Galioto, Matt Boler, John D. Jakeman, Cosmin Safta, Alex A. Gorodetsky
2024	J	`jnl`	Democratizing Uncertainty Quantification. CoRR 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
2024	J	`jnl`	Grouped approximate control variate estimators. CoRR Alex A. Gorodetsky, John D. Jakeman, Michael S. Eldred
2024	J	`jnl`	Kernel Neural Operators (KNOs) for Scalable, Memory-efficient, Geometrically-flexible Operator Learning. CoRR Matthew Lowery, John Turnage, Zachary Morrow, John D. Jakeman, Akil Narayan, Shandian Zhe, Varun Shankar
2023	J	`jnl`	Epistemic Uncertainty-Aware Barlow Twins Reduced Order Modeling for Nonlinear Contact Problems. IEEE Access Teeratorn Kadeethum, John D. Jakeman, Youngsoo Choi, Nikolaos Bouklas, Hongkyu Yoon
2023	J	`jnl`	PyApprox: A software package for sensitivity analysis, Bayesian inference, optimal experimental design, and multi-fidelity uncertainty quantification and surrogate modeling. Environ. Model. Softw. John D. Jakeman
2022	J	`jnl`	Assessing the predictive impact of factor fixing with an adaptive uncertainty-based approach. Environ. Model. Softw. Qian Wang, Joseph H. A. Guillaume, John D. Jakeman, Tao Yang, Takuya Iwanaga, Barry F. W. Croke, Anthony J. Jakeman
2022	J	`jnl`	Reverse-mode differentiation in arbitrary tensor network format: with application to supervised learning. J. Mach. Learn. Res. Alex A. Gorodetsky, Cosmin Safta, John D. Jakeman
2022	J	`jnl`	Risk-Adapted Optimal Experimental Design. SIAM/ASA J. Uncertain. Quantification Drew P. Kouri, John D. Jakeman, J. Gabriel Huerta
2022	J	`jnl`	Surrogate modeling for efficiently, accurately and conservatively estimating measures of risk. Reliab. Eng. Syst. Saf. John D. Jakeman, Drew P. Kouri, J. Gabriel Huerta
2021	J	`jnl`	Data-Driven Learning of Nonautonomous Systems. SIAM J. Sci. Comput. Tong Qin, Zhen Chen, John D. Jakeman, Dongbin Xiu
2021	J	`jnl`	The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support. Environ. Model. Softw. 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
2020	J	`jnl`	A Survey of Constrained Gaussian Process Regression: Approaches and Implementation Challenges. CoRR Laura P. Swiler, Mamikon A. Gulian, Ari Frankel, Cosmin Safta, John D. Jakeman
2020	J	`jnl`	A generalized approximate control variate framework for multifidelity uncertainty quantification. J. Comput. Phys. Alex A. Gorodetsky, Gianluca Geraci, Michael S. Eldred, John D. Jakeman
2020	J	`jnl`	Data-driven learning of non-autonomous systems. CoRR Tong Qin, Zhen Chen, John D. Jakeman, Dongbin Xiu
2020	J	`jnl`	MFNets: Learning network representations for multifidelity surrogate modeling. CoRR Alex A. Gorodetsky, John D. Jakeman, Gianluca Geraci
2020	J	`jnl`	Optimal experimental design for prediction based on push-forward probability measures. J. Comput. Phys. Troy D. Butler, John D. Jakeman, Tim Wildey
2019	J	`jnl`	A neural network approach for uncertainty quantification for time-dependent problems with random parameters. CoRR Tong Qin, Zhen Chen, John D. Jakeman, Dongbin Xiu
2019	J	`jnl`	Adaptive Multi-index Collocation for Uncertainty Quantification and Sensitivity Analysis. CoRR John D. Jakeman, Michael Eldred, Gianluca Geraci, Alex A. Gorodetsky
2019	J	`jnl`	Introductory overview of identifiability analysis: A guide to evaluating whether you have the right type of data for your modeling purpose. Environ. Model. Softw. 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
2018	J	`jnl`	Combining Push-Forward Measures and Bayes' Rule to Construct Consistent Solutions to Stochastic Inverse Problems. SIAM J. Sci. Comput. Troy D. Butler, John D. Jakeman, Tim Wildey
2018	J	`jnl`	Compressed Sensing with Sparse Corruptions: Fault-Tolerant Sparse Collocation Approximations. SIAM/ASA J. Uncertain. Quantification Ben Adcock, Anyi Bao, John D. Jakeman, Akil Narayan
2018	J	`jnl`	Convergence of Probability Densities Using Approximate Models for Forward and Inverse Problems in Uncertainty Quantification. SIAM J. Sci. Comput. Troy D. Butler, John D. Jakeman, Tim Wildey
2018	J	`jnl`	Gradient-based optimization for regression in the functional tensor-train format. J. Comput. Phys. Alex A. Gorodetsky, John D. Jakeman
2017	J	`jnl`	A Christoffel function weighted least squares algorithm for collocation approximations. Math. Comput. Akil Narayan, John D. Jakeman, Tao Zhou
2017	J	`jnl`	A Generalized Sampling and Preconditioning Scheme for Sparse Approximation of Polynomial Chaos Expansions. SIAM J. Sci. Comput. John D. Jakeman, Akil Narayan, Tao Zhou
2015	J	`jnl`	Enhancing adaptive sparse grid approximations and improving refinement strategies using adjoint-based a posteriori error estimates. J. Comput. Phys. John D. Jakeman, Timothy Wildey
2015	J	`jnl`	Enhancing ℓ1-minimization estimates of polynomial chaos expansions using basis selection. J. Comput. Phys. John D. Jakeman, Michael S. Eldred, Khachik Sargsyan
2015	J	`jnl`	Local Polynomial Chaos Expansion for Linear Differential Equations with High Dimensional Random Inputs. SIAM J. Sci. Comput. Yi Chen, John D. Jakeman, Claude Jeffrey Gittelson, Dongbin Xiu
2014	J	`jnl`	Adaptive Leja Sparse Grid Constructions for Stochastic Collocation and High-Dimensional Approximation. SIAM J. Sci. Comput. Akil Narayan, John D. Jakeman
2014	J	`jnl`	Enhancing adaptive sparse grid approximations and improving refinement strategies using adjoint-based a posteriori error estimates. CoRR John D. Jakeman, Timothy Wildey
2014	J	`jnl`	Enhancing ℓ CoRR John D. Jakeman, Michael S. Eldred, Khachik Sargsyan
2013	J	`jnl`	Minimal multi-element stochastic collocation for uncertainty quantification of discontinuous functions. J. Comput. Phys. John D. Jakeman, Akil Narayan, Dongbin Xiu
2011	J	`jnl`	Characterization of discontinuities in high-dimensional stochastic problems on adaptive sparse grids. J. Comput. Phys. John D. Jakeman, Richard Archibald, Dongbin Xiu
2011	J	`jnl`	Local and Dimension Adaptive Sparse Grid Interpolation and Quadrature CoRR John D. Jakeman, Stephen G. Roberts
2010	J	`jnl`	Numerical approach for quantification of epistemic uncertainty. J. Comput. Phys. John D. Jakeman, Michael S. Eldred, Dongbin Xiu
2006	Misc	`conf`	Simulation of Tsunami and Flash Floods. HPSC Stephen Gwyn Roberts, Ole Møller Nielsen, John D. Jakeman