Simulation of Tsunami and Flash Floods. - Stephen Gwyn Roberts; Ole Møller Nielsen; John D. Jakeman (HPSC, 2006)

Numerical approach for quantification of epistemic uncertainty. - John D. Jakeman; Michael S. Eldred; Dongbin Xiu (J. Comput. Phys., 2010)

Local and Dimension Adaptive Sparse Grid Interpolation and Quadrature - John D. Jakeman; Stephen G. Roberts (CoRR, 2011)

Characterization of discontinuities in high-dimensional stochastic problems on adaptive sparse grids. - John D. Jakeman; Richard Archibald; Dongbin Xiu (J. Comput. Phys., 2011)

Minimal multi-element stochastic collocation for uncertainty quantification of discontinuous functions. - John D. Jakeman; Akil Narayan 001; Dongbin Xiu (J. Comput. Phys., 2013)

Enhancing ℓ1-minimization estimates of polynomial chaos expansions using basis selection. - John D. Jakeman; Michael S. Eldred; Khachik Sargsyan (CoRR, 2014)

Adaptive Leja Sparse Grid Constructions for Stochastic Collocation and High-Dimensional Approximation. - Akil Narayan 001; John D. Jakeman (SIAM J. Sci. Comput., 2014)

Enhancing ℓ1-minimization estimates of polynomial chaos expansions using basis selection. - John D. Jakeman; Michael S. Eldred; Khachik Sargsyan (J. Comput. Phys., 2015)

Enhancing adaptive sparse grid approximations and improving refinement strategies using adjoint-based a posteriori error estimates. - John D. Jakeman; Timothy Wildey (J. Comput. Phys., 2015)

Local Polynomial Chaos Expansion for Linear Differential Equations with High Dimensional Random Inputs. - Yi Chen; John D. Jakeman; Claude Jeffrey Gittelson; Dongbin Xiu (SIAM J. Sci. Comput., 2015)

A Christoffel function weighted least squares algorithm for collocation approximations. - Akil Narayan 001; John D. Jakeman; Tao Zhou 010 (Math. Comput., 2017)

A Generalized Sampling and Preconditioning Scheme for Sparse Approximation of Polynomial Chaos Expansions. - John D. Jakeman; Akil Narayan 001; Tao Zhou 010 (SIAM J. Sci. Comput., 2017)

Gradient-based optimization for regression in the functional tensor-train format. - Alex A. Gorodetsky; John D. Jakeman (J. Comput. Phys., 2018)

Compressed Sensing with Sparse Corruptions: Fault-Tolerant Sparse Collocation Approximations. - Ben Adcock; Anyi Bao; John D. Jakeman; Akil Narayan 001 (SIAM/ASA J. Uncertain. Quantification, 2018)

Combining Push-Forward Measures and Bayes' Rule to Construct Consistent Solutions to Stochastic Inverse Problems. - Troy D. Butler; John D. Jakeman; Tim Wildey (SIAM J. Sci. Comput., 2018)

Convergence of Probability Densities Using Approximate Models for Forward and Inverse Problems in Uncertainty Quantification. - Troy D. Butler; John D. Jakeman; Tim Wildey (SIAM J. Sci. Comput., 2018)

Adaptive Multi-index Collocation for Uncertainty Quantification and Sensitivity Analysis. - John D. Jakeman; Michael Eldred; Gianluca Geraci; Alex A. Gorodetsky (CoRR, 2019)

A neural network approach for uncertainty quantification for time-dependent problems with random parameters. - Tong Qin 002; Zhen Chen; John D. Jakeman; Dongbin Xiu (CoRR, 2019)

Introductory overview of identifiability analysis: A guide to evaluating whether you have the right type of data for your modeling purpose. - 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 (Environ. Model. Softw., 2019)

Data-driven learning of non-autonomous systems. - Tong Qin 002; Zhen Chen; John D. Jakeman; Dongbin Xiu (CoRR, 2020)

A Survey of Constrained Gaussian Process Regression: Approaches and Implementation Challenges. - Laura P. Swiler; Mamikon A. Gulian; Ari Frankel; Cosmin Safta; John D. Jakeman (CoRR, 2020)

MFNets: Learning network representations for multifidelity surrogate modeling. - Alex A. Gorodetsky; John D. Jakeman; Gianluca Geraci (CoRR, 2020)

Optimal experimental design for prediction based on push-forward probability measures. - Troy D. Butler; John D. Jakeman; Tim Wildey (J. Comput. Phys., 2020)

A generalized approximate control variate framework for multifidelity uncertainty quantification. - Alex A. Gorodetsky; Gianluca Geraci; Michael S. Eldred; John D. Jakeman (J. Comput. Phys., 2020)

The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support. - 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; et al. (Environ. Model. Softw., 2021)

Data-Driven Learning of Nonautonomous Systems. - Tong Qin 002; Zhen Chen; John D. Jakeman; Dongbin Xiu (SIAM J. Sci. Comput., 2021)

Assessing the predictive impact of factor fixing with an adaptive uncertainty-based approach. - Qian Wang; Joseph H. A. Guillaume; John D. Jakeman; Tao Yang; Takuya Iwanaga; Barry F. W. Croke; Anthony J. Jakeman (Environ. Model. Softw., 2022)

Reverse-mode differentiation in arbitrary tensor network format: with application to supervised learning. - Alex A. Gorodetsky; Cosmin Safta; John D. Jakeman (J. Mach. Learn. Res., 2022)

Risk-Adapted Optimal Experimental Design. - Drew P. Kouri; John D. Jakeman; J. Gabriel Huerta (SIAM/ASA J. Uncertain. Quantification, 2022)

Surrogate modeling for efficiently, accurately and conservatively estimating measures of risk. - John D. Jakeman; Drew P. Kouri; J. Gabriel Huerta (Reliab. Eng. Syst. Saf., 2022)

Epistemic Uncertainty-Aware Barlow Twins Reduced Order Modeling for Nonlinear Contact Problems. - Teeratorn Kadeethum; John D. Jakeman; Youngsoo Choi; Nikolaos Bouklas; Hongkyu Yoon (IEEE Access, 2023)

PyApprox: A software package for sensitivity analysis, Bayesian inference, optimal experimental design, and multi-fidelity uncertainty quantification and surrogate modeling. - John D. Jakeman (Environ. Model. Softw., 2023)

Democratizing Uncertainty Quantification. - 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; et al. (CoRR, 2024)

Grouped approximate control variate estimators. - Alex A. Gorodetsky; John D. Jakeman; Michael S. Eldred (CoRR, 2024)

Kernel Neural Operators (KNOs) for Scalable, Memory-efficient, Geometrically-flexible Operator Learning. - Matthew Lowery; John Turnage; Zachary Morrow; John D. Jakeman; Akil Narayan 001; Shandian Zhe; Varun Shankar (CoRR, 2024)

A switching Kalman filter approach to online mitigation and correction of sensor corruption for inertial navigation. - Artem Mustaev; Nicholas Galioto; Matt Boler; John D. Jakeman; Cosmin Safta; Alex A. Gorodetsky (CoRR, 2024)

Democratizing uncertainty quantification. - 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; et al. (J. Comput. Phys., 2025)

Verification and Validation for Trustworthy Scientific Machine Learning. - John D. Jakeman; Lorena A. Barba; Joaquim R. R. A. Martins; Thomas O'Leary-Roseberry (CoRR, 2025)