Publications

66 journal papers, conference papers, and preprints in uncertainty quantification, surrogate modeling, and scientific computing.

2026

Verification and validation for trustworthy scientific machine learning

J.D. Jakeman, L.A. Barba, J.R.R.A. Martins, T. O'Leary-Roseberry

Machine Learning: Science and Technology 7:025055
DOI arXiv

Optimal experimental design criteria for data-consistent inversion

T. Butler, J.D. Jakeman, M. Pilosov, S. Walsh, T. Wildey

International Journal for Uncertainty Quantification

Investigating the impacts of parameter fixing on a spatially distributed water quality model with an adaptive emulation approach

Q. Wang, J.H.A. Guillaume, J.D. Jakeman, et al.

Environmental Modelling & Software 202:107001

2025

Building population-informed priors for Bayesian inference using data-consistent stochastic inversion

R.D. White, J.D. Jakeman, T. Wildey, T. Butler

SIAM/ASA Journal on Uncertainty Quantification 13(3):1475–1500
DOI

An evaluation of multi-fidelity methods for quantifying uncertainty in projections of ice-sheet mass change

J.D. Jakeman, M. Perego, D.T. Seidl, T.A. Hartland, T.R. Hillebrand, et al.

Earth System Dynamics 16:513
DOI

Democratizing uncertainty quantification

L. Seelinger, A. Reinarz, M.B. Lykkegaard, R. Akers, et al.

Journal of Computational Physics

Boosting efficiency and reducing graph reliance in multifidelity surrogate networks

X. Zeng, G. Geraci, A.A. Gorodetsky, J.D. Jakeman, R. Ghanem

Computer Methods in Applied Mechanics and Engineering

Multi-fidelity Bayesian networks for transfer learning in modular systems

J. Wentz, G. Geraci, O. Davis, M. Eldred, J.D. Jakeman, A. Mustaev, A. Gorodetsky

AIAA SciTech Forum
DOI

An optimal weighted least-squares method for operator learning

J. Turnage, M. Lowery, J.D. Jakeman, Z. Morrow, A. Narayan, V. Shankar

arXiv preprint
arXiv

SUPN: Shallow universal polynomial networks

Z. Morrow, M. Penwarden, B. Chen, A. Javeed, A. Narayan, J.D. Jakeman

arXiv preprint
arXiv

Optimally balancing exploration and exploitation in multifidelity UQ

T. Dixon, A. Gorodetsky, J.D. Jakeman, A. Narayan, Y. Xu

arXiv preprint

Kernel neural operators (KNOs)

M. Lowery, J. Turnage, Z. Morrow, J.D. Jakeman, A. Narayan, S. Zhe, V. Shankar

arXiv preprint

Basic Research Needs for Inverse Methods for Complex Systems under Uncertainty

J. Donatelli, J.D. Jakeman, M. Shields, A. Gelb, F. Herrmann, S. Jantre, et al.

DOE ASCR Workshop Report
Report

2024

Probabilistic projections of the Amery Ice Shelf catchment, Antarctica, under conditions of high ice-shelf basal melt

S. Jantre, M.J. Hoffman, N.M. Urban, T. Hillebrand, M. Perego, S. Price, J.D. Jakeman, et al.

The Cryosphere 18:5207
DOI

Assessing convergence in global sensitivity analysis: a review of methods

X. Sun, A.J. Jakeman, B.F.W. Croke, S.G. Roberts, J.D. Jakeman

Socio-Environmental Systems Modelling
DOI

Hyperdifferential sensitivity analysis in the context of Bayesian inference applied to ice-sheet problems

W. Reese, J. Hart, B.B. Waanders, M. Perego, J.D. Jakeman, A. Saibaba

International Journal for Uncertainty Quantification 14(1)
DOI

Grouped approximate control variate estimators

A.A. Gorodetsky, J.D. Jakeman, M.S. Eldred

arXiv preprint

A switching Kalman filter approach to online mitigation of distribution shift

A. Mustaev, N. Galioto, M. Boler, J.D. Jakeman, C. Safta, A. Gorodetsky

arXiv preprint

2023

PyApprox: A software package for sensitivity analysis, Bayesian inference, optimal experimental design, and multi-fidelity uncertainty quantification and surrogate modeling

J.D. Jakeman

Environmental Modelling & Software 170:105825
DOI

Multifidelity uncertainty quantification with models based on dissimilar parameters

X. Zeng, G. Geraci, M.S. Eldred, J.D. Jakeman, A.A. Gorodetsky, R. Ghanem

Computer Methods in Applied Mechanics and Engineering 415:116205
DOI

A decision-relevant factor-fixing framework: application to uncertainty analysis of a high-dimensional water quality model

Q. Wang, J.H.A. Guillaume, J.D. Jakeman, F.R. Bennett, B.F.W. Croke, B. Fu, et al.

Water Resources Research 59
DOI

Epistemic uncertainty-aware Barlow Twins reduced order modeling for nonlinear contact problems

T. Kadeethum, J.D. Jakeman, Y. Choi, N. Bouklas, H. Yoon

IEEE Access
DOI

Improving Bayesian networks multifidelity surrogate construction with basis adaptation

X. Zeng, G. Geraci, A.A. Gorodetsky, J.D. Jakeman, M.S. Eldred, R.G. Ghanem

AIAA SciTech Forum
DOI

Strategies for automation of model tuning in multi-fidelity trajectory uncertainty propagation

M. Thompson, G. Geraci, G. Bomarito, J. Warner, P. Leser, W.P. Leser, M.S. Eldred, J.D. Jakeman, A.A. Gorodetsky

AIAA SciTech Forum
DOI

2022

Global sensitivity analysis using the ultra-low resolution energy exascale Earth System Model

I. Tezaur, K. Peterson, A. Powell, J.D. Jakeman, E. Roesler

Journal of Advances in Modeling Earth Systems 14(8)
DOI

Adaptive experimental design for multi-fidelity surrogate modeling of multi-disciplinary systems

J.D. Jakeman, S. Friedman, M.S. Eldred, L. Tamellini, A.A. Gorodetsky, D.J. Allaire

International Journal for Numerical Methods in Engineering 123(12)
DOI

Risk-adapted optimal experimental design

D.P. Kouri, J.D. Jakeman, J.G. Huerta

SIAM/ASA Journal on Uncertainty Quantification 10(2)
DOI

Surrogate modeling for efficiently, accurately and conservatively estimating measures of risk

J.D. Jakeman, D.P. Kouri, J.G. Huerta

Reliability Engineering & System Safety 221:108280
DOI

Assessing the predictive impact of factor fixing with an adaptive uncertainty-based approach

Q. Wang, J.H.A. Guillaume, J.D. Jakeman, T. Yang, T. Iwanaga, B. Croke, et al.

Environmental Modelling & Software 148:105290
DOI

Reverse-mode differentiation in arbitrary tensor network format: with application to supervised learning

A. Gorodetsky, C. Safta, J.D. Jakeman

Journal of Machine Learning Research 23(1)

Improving multi-model trajectory simulation estimators using model selection and tuning

G.F. Bomarito, G. Geraci, J.E. Warner, P.E. Leser, W.P. Leser, M.S. Eldred, J.D. Jakeman, A.A. Gorodetsky

AIAA SciTech Forum
DOI

2021

MFNets: data efficient all-at-once learning of multifidelity surrogates as directed networks of information sources

A.A. Gorodetsky, J.D. Jakeman, G. Geraci

Computational Mechanics 68:741–758
DOI

Cholesky-based experimental design for Gaussian process and kernel-based emulation and calibration

H. Harbrecht, J.D. Jakeman, P. Zaspel

Communications in Computational Physics 29(4)
DOI

The future of sensitivity analysis: an essential discipline for systems modeling and policy support

S. Razavi, A. Jakeman, A. Saltelli, C. Prieur, B. Iooss, E. Borgonovo, et al.

Environmental Modelling & Software 137:104954
DOI

Data-driven learning of nonautonomous systems

T. Qin, Z. Chen, J.D. Jakeman, D. Xiu

SIAM Journal on Scientific Computing 43(3)
DOI

Deep learning of parameterized equations with applications to uncertainty quantification

T. Qin, Z. Chen, J.D. Jakeman, D. Xiu

International Journal for Uncertainty Quantification 11(2)

Randomized algorithms for scientific computing (RASC)

A. Buluç, T.G. Kolda, S.M. Wild, M. Anitescu, A. DeGennaro, J.D. Jakeman, et al.

DOE ASCR Workshop Report
DOI arXiv

2020

A survey of constrained Gaussian process regression: approaches and implementation challenges

L.P. Swiler, M. Gulian, A.L. Frankel, C. Safta, J.D. Jakeman

Journal of Machine Learning for Modeling and Computing 1(2)

A generalized approximate control variate framework for multifidelity uncertainty quantification

A.A. Gorodetsky, G. Geraci, M.S. Eldred, J.D. Jakeman

Journal of Computational Physics 408:109257

Adaptive multi-index collocation for uncertainty quantification and sensitivity analysis

J.D. Jakeman, M.S. Eldred, G. Geraci, A. Gorodetsky

International Journal for Numerical Methods in Engineering 121:1314–1343
DOI

MFNets: multi-fidelity data-driven networks for Bayesian learning and prediction

A.A. Gorodetsky, J.D. Jakeman, G. Geraci, M.S. Eldred

International Journal for Uncertainty Quantification 10(6)

Modeling water quality in watersheds: from here to the next generation

B. Fu, J.S. Horsburgh, A.J. Jakeman, C. Gualtieri, T. Arnold, L. Marshall, J.D. Jakeman, et al.

Water Resources Research 56(11)

Optimal experimental design for prediction based on push-forward probability measures

T. Butler, J.D. Jakeman, T. Wildey

Journal of Computational Physics 416:109518

Non-destructive simulation of node defects in additively manufactured lattice structures

B. Lozanovski, D. Downing, R. Tino, A. Du Plessis, P. Tran, J.D. Jakeman, et al.

Additive Manufacturing 36:101593

2019

Introductory overview of identifiability analysis: a guide to evaluating whether you have the right type of data for your modeling purpose

J.H.A. Guillaume, J.D. Jakeman, S. Marsili-Libelli, M. Asher, P. Brunner, et al.

Environmental Modelling & Software 119:418–432

Polynomial chaos expansions for dependent random variables

J.D. Jakeman, F. Franzelin, A. Narayan, M. Eldred, D. Pflüger

Computer Methods in Applied Mechanics and Engineering 351:643–666

Recent advancements in multilevel-multifidelity techniques for forward UQ in the DARPA Sequoia project

G. Geraci, M.S. Eldred, A.A. Gorodetsky, J.D. Jakeman

AIAA SciTech Forum
DOI

2018

Combining push-forward measures and Bayes' rule to construct consistent solutions to stochastic inverse problems

T. Butler, J.D. Jakeman, T. Wildey

SIAM Journal on Scientific Computing 40(2):A984–A1011

Convergence of probability densities using approximate models for forward and inverse problems in uncertainty quantification

T. Butler, J.D. Jakeman, T. Wildey

SIAM Journal on Scientific Computing 40(5):A3523–A3548

Gradient-based optimization for regression in the functional tensor-train format

A.A. Gorodetsky, J.D. Jakeman

Journal of Computational Physics 374:1219–1238

Generation and application of multivariate polynomial quadrature rules

J.D. Jakeman, A. Narayan

Computer Methods in Applied Mechanics and Engineering 338:134–161

Optimal experimental design using a consistent Bayesian approach

S.N. Walsh, T.M. Wildey, J.D. Jakeman

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B 4(1)

Compressed sensing with sparse corruptions: fault-tolerant sparse collocation approximations

B. Adcock, A. Bao, J.D. Jakeman, A. Narayan

SIAM/ASA Journal on Uncertainty Quantification 6(4):1424–1453

Time and frequency domain methods for basis selection in random linear dynamical systems

J.D. Jakeman, R. Pulch

International Journal for Uncertainty Quantification 8(6)

Multilevel-multifidelity approaches for forward UQ in the DARPA SEQUOIA project

M.S. Eldred, G. Geraci, A. Gorodetsky, J.D. Jakeman

AIAA SciTech Forum
DOI

2017

A generalized sampling and preconditioning scheme for sparse approximation of polynomial chaos expansions

J.D. Jakeman, A. Narayan, T. Zhou

SIAM Journal on Scientific Computing 39(3):A1114–A1144

A Christoffel function weighted least squares algorithm for collocation approximations

A. Narayan, J.D. Jakeman, T. Zhou

Mathematics of Computation 86(306):1913–1947

Scalable environment for quantification of uncertainty and optimization in industrial applications (SEQUOIA)

J.J. Alonso, M.S. Eldred, P. Constantine, K. Duraisamy, C. Farhat, G. Iaccarino, J.D. Jakeman, et al.

AIAA SciTech Forum
DOI

2015

Enhancing ℓ₁-minimization estimates of polynomial chaos expansions using basis selection

J.D. Jakeman, M.S. Eldred, K. Sargsyan

Journal of Computational Physics 289:18–34
DOI

Local polynomial chaos expansion for linear differential equations with high-dimensional random inputs

Y. Chen, J.D. Jakeman, C. Gittelson, D. Xiu

SIAM Journal on Scientific Computing 37(1):A79–A102

Enhancing adaptive sparse grid approximations and improving refinement strategies using adjoint-based a posteriori error estimates

J.D. Jakeman, T. Wildey

Journal of Computational Physics 280:54–71

2014

Adaptive Leja sparse grid constructions for stochastic collocation and high-dimensional approximation

A. Narayan, J.D. Jakeman

SIAM Journal on Scientific Computing 36(6):A2952–A2983

2013

Minimal multi-element stochastic collocation for uncertainty quantification of discontinuous functions

J.D. Jakeman, A. Narayan, D. Xiu

Journal of Computational Physics 242:790–811

2011

Characterization of discontinuities in high-dimensional stochastic problems on adaptive sparse grids

J.D. Jakeman, R. Archibald, D. Xiu

Journal of Computational Physics 230(10):3977–3997

2010

Numerical approach for quantification of epistemic uncertainty

J.D. Jakeman, M. Eldred, D. Xiu

Journal of Computational Physics 229(12):4648–4663

Towards spatially distributed quantitative assessment of tsunami inundation models

J.D. Jakeman, O.M. Nielsen, K.V. Putten, R. Mleczko, D. Burbidge, N. Horspool

Ocean Dynamics 60:1115–1138