@misc{morrow2026wildfire,
  author    = {Morrow, Z. and Crockett, J. and Jakeman, J. D. and Krofcheck, D. J.},
  title     = {Enabling real-time training of a wildfire-to-smoke map with multilinear operators},
  year      = {2026},
  eprint    = {2605.04164},
  archiveprefix = {arXiv},
  url       = {https://arxiv.org/abs/2605.04164},
}

@article{jakeman2026vv,
  author    = {Jakeman, J. D. and Barba, L. A. and Martins, J. R. R. A. and O'Leary-Roseberry, T.},
  title     = {Verification and validation for trustworthy scientific machine learning},
  journal   = {Machine Learning: Science and Technology},
  year      = {2026},
  volume    = {7},
  pages     = {025055},
  doi       = {10.1088/2632-2153/ae59ec},
  url       = {https://doi.org/10.1088/2632-2153/ae59ec},
  eprint    = {2502.15496},
  archiveprefix = {arXiv},
}

@article{butler2026oed,
  author    = {Butler, T. and Jakeman, J. D. and Pilosov, M. and Walsh, S. and Wildey, T.},
  title     = {Optimal experimental design criteria for data-consistent inversion},
  journal   = {International Journal for Uncertainty Quantification},
  year      = {2026},
  volume    = {16},
  number    = {2},
  pages     = {75--100},
  doi       = {10.1615/Int.J.UncertaintyQuantification.2025060023},
  url       = {https://doi.org/10.1615/Int.J.UncertaintyQuantification.2025060023},
  eprint    = {2506.12157},
  archiveprefix = {arXiv},
}

@article{wang2026parameter,
  author    = {Wang, Q. and Guillaume, J. H. A. and Jakeman, J. D. and Guo, D. and Singh, A. and Croke, B. F. W. and Jakeman, A. J.},
  title     = {Investigating the impacts of parameter fixing on the performance of a spatially distributed water quality model with an adaptive emulation approach},
  journal   = {Environmental Modelling \& Software},
  year      = {2026},
  volume    = {202},
  pages     = {107001},
  doi       = {10.1016/j.envsoft.2026.107001},
  url       = {https://doi.org/10.1016/j.envsoft.2026.107001},
}

@article{white2025population,
  author    = {White, R. D. and Jakeman, J. D. and Wildey, T. and Butler, T.},
  title     = {Building population-informed priors for {Bayesian} inference using data-consistent stochastic inversion},
  journal   = {SIAM/ASA Journal on Uncertainty Quantification},
  year      = {2025},
  volume    = {13},
  number    = {3},
  pages     = {1475--1500},
  doi       = {10.1137/24M1678234},
  url       = {https://doi.org/10.1137/24M1678234},
  eprint    = {2407.13814},
  archiveprefix = {arXiv},
}

@article{jakeman2025icesheet,
  author    = {Jakeman, J. D. and Perego, M. and Seidl, D. T. and Hartland, T. A. and Hillebrand, T. R. and others},
  title     = {An evaluation of multi-fidelity methods for quantifying uncertainty in projections of ice-sheet mass change},
  journal   = {Earth System Dynamics},
  year      = {2025},
  volume    = {16},
  pages     = {513},
  doi       = {10.5194/esd-16-513-2025},
  url       = {https://doi.org/10.5194/esd-16-513-2025},
}

@article{seelinger2025democratizing,
  author    = {Seelinger, L. and Reinarz, A. and Lykkegaard, M. B. and Akers, R. and others},
  title     = {Democratizing uncertainty quantification},
  journal   = {Journal of Computational Physics},
  year      = {2025},
  volume    = {521},
  pages     = {113542},
  doi       = {10.1016/j.jcp.2024.113542},
  url       = {https://doi.org/10.1016/j.jcp.2024.113542},
  eprint    = {2402.13768},
  archiveprefix = {arXiv},
}

@article{zeng2025boosting,
  author    = {Zeng, X. and Geraci, G. and Gorodetsky, A. A. and Jakeman, J. D. and Ghanem, R.},
  title     = {Boosting efficiency and reducing graph reliance: Basis adaptation integration in {Bayesian} multi-fidelity networks},
  journal   = {Computer Methods in Applied Mechanics and Engineering},
  year      = {2025},
  doi       = {10.1016/j.cma.2024.117657},
  url       = {https://doi.org/10.1016/j.cma.2024.117657},
}

@inproceedings{wentz2025mfbn,
  author    = {Wentz, J. and Geraci, G. and Davis, O. and Eldred, M. and Jakeman, J. D. and Mustaev, A. and Gorodetsky, A.},
  title     = {Multi-fidelity {Bayesian} networks for transfer learning in modular systems},
  booktitle = {AIAA SciTech Forum},
  year      = {2025},
  doi       = {10.2514/6.2025-1961},
  url       = {https://doi.org/10.2514/6.2025-1961},
}

@article{turnage2025optimal,
  author    = {Turnage, J. and Lowery, M. and Jakeman, J. D. and Morrow, Z. and Narayan, A. and Shankar, V.},
  title     = {An optimal weighted least-squares method for operator learning},
  journal   = {arXiv preprint},
  year      = {2025},
  eprint    = {2512.11168},
  archiveprefix = {arXiv},
  url       = {https://arxiv.org/abs/2512.11168},
}

@article{morrow2025supn,
  author    = {Morrow, Z. and Penwarden, M. and Chen, B. and Javeed, A. and Narayan, A. and Jakeman, J. D.},
  title     = {{SUPN}: Shallow universal polynomial networks},
  journal   = {arXiv preprint},
  year      = {2025},
  eprint    = {2511.21414},
  archiveprefix = {arXiv},
  url       = {https://arxiv.org/abs/2511.21414},
}

@misc{dixon2026exploration,
  author    = {Dixon, T. and Gorodetsky, A. and Jakeman, J. D. and Narayan, A. and Xu, Y.},
  title     = {Optimally balancing exploration and exploitation to automate multi-fidelity statistical estimation},
  year      = {2026},
  eprint    = {2505.09828},
  archiveprefix = {arXiv},
  url       = {https://arxiv.org/abs/2505.09828},
}

@article{lowery2026kno,
  author    = {Lowery, M. and Turnage, J. and Morrow, Z. and Jakeman, J. D. and Narayan, A. and Zhe, S. and Shankar, V.},
  title     = {Kernel neural operators ({KNOs}) for scalable, memory-efficient, geometrically-flexible operator learning},
  journal   = {Transactions on Machine Learning Research},
  year      = {2026},
  issn      = {2835-8856},
  url       = {https://openreview.net/forum?id=Q0C4jYZQ7x},
  eprint    = {2407.00809},
  archiveprefix = {arXiv},
}

@techreport{donatelli2025inverse,
  author    = {Donatelli, J. and Jakeman, J. D. and Shields, M. and Gelb, A. and Herrmann, F. and Jantre, S. and others},
  title     = {Basic Research Needs for Inverse Methods for Complex Systems under Uncertainty},
  institution = {U.S. Department of Energy, ASCR},
  year      = {2025},
  url       = {https://www.osti.gov/servlets/purl/2583339},
}

@article{jantre2024amery,
  author    = {Jantre, S. and Hoffman, M. J. and Urban, N. M. and Hillebrand, T. and Perego, M. and Price, S. and Jakeman, J. D. and others},
  title     = {Probabilistic projections of the {Amery Ice Shelf} catchment, {Antarctica}, under conditions of high ice-shelf basal melt},
  journal   = {The Cryosphere},
  year      = {2024},
  volume    = {18},
  pages     = {5207},
  doi       = {10.5194/tc-18-5207-2024},
  url       = {https://doi.org/10.5194/tc-18-5207-2024},
}

@article{sun2024convergence,
  author    = {Sun, X. and Jakeman, A. J. and Croke, B. F. W. and Roberts, S. G. and Jakeman, J. D.},
  title     = {Assessing convergence in global sensitivity analysis: a review of methods for assessing and monitoring convergence},
  journal   = {Socio-Environmental Systems Modelling},
  year      = {2024},
  doi       = {10.18174/sesmo.18678},
  url       = {https://doi.org/10.18174/sesmo.18678},
}

@article{reese2024hyperdifferential,
  author    = {Reese, W. and Hart, J. and Waanders, B. B. and Perego, M. and Jakeman, J. D. and Saibaba, A.},
  title     = {Hyperdifferential sensitivity analysis in the context of {Bayesian} inference applied to ice-sheet problems},
  journal   = {International Journal for Uncertainty Quantification},
  year      = {2024},
  volume    = {14},
  number    = {1},
  doi       = {10.1615/int.j.uncertaintyquantification.2023047605},
  url       = {https://doi.org/10.1615/int.j.uncertaintyquantification.2023047605},
}

@article{gorodetsky2024grouped,
  author    = {Gorodetsky, A. A. and Jakeman, J. D. and Eldred, M. S.},
  title     = {Grouped approximate control variate estimators},
  journal   = {arXiv preprint},
  year      = {2024},
  eprint    = {2402.14736},
  archiveprefix = {arXiv},
  url       = {https://arxiv.org/abs/2402.14736},
}

@misc{mustaev2024switching,
  author    = {Mustaev, A. and Galioto, N. and Boler, M. and Jakeman, J. D. and Safta, C. and Gorodetsky, A.},
  title     = {A switching {Kalman} filter approach to online mitigation and correction of sensor corruption for inertial navigation},
  year      = {2024},
  eprint    = {2412.06601},
  archiveprefix = {arXiv},
  url       = {https://arxiv.org/abs/2412.06601},
}

@article{jakeman2023pyapprox,
  author    = {Jakeman, J. D.},
  title     = {{PyApprox}: A software package for sensitivity analysis, {Bayesian} inference, optimal experimental design, and multi-fidelity uncertainty quantification and surrogate modeling},
  journal   = {Environmental Modelling \& Software},
  year      = {2023},
  volume    = {170},
  pages     = {105825},
  doi       = {10.1016/j.envsoft.2023.105825},
  url       = {https://doi.org/10.1016/j.envsoft.2023.105825},
}

@article{zeng2023dissimilar,
  author    = {Zeng, X. and Geraci, G. and Eldred, M. S. and Jakeman, J. D. and Gorodetsky, A. A. and Ghanem, R.},
  title     = {Multifidelity uncertainty quantification with models based on dissimilar parameters},
  journal   = {Computer Methods in Applied Mechanics and Engineering},
  year      = {2023},
  volume    = {415},
  pages     = {116205},
  doi       = {10.1016/j.cma.2023.116205},
  url       = {https://doi.org/10.1016/j.cma.2023.116205},
}

@article{wang2023factorfixing,
  author    = {Wang, Q. and Guillaume, J. H. A. and Jakeman, J. D. and Bennett, F. R. and Croke, B. F. W. and Fu, B. and others},
  title     = {A decision-relevant factor-fixing framework: application to uncertainty analysis of a high-dimensional water quality model},
  journal   = {Water Resources Research},
  year      = {2023},
  volume    = {59},
  doi       = {10.1029/2022wr032194},
  url       = {https://doi.org/10.1029/2022wr032194},
}

@article{kadeethum2023barlow,
  author    = {Kadeethum, T. and Jakeman, J. D. and Choi, Y. and Bouklas, N. and Yoon, H.},
  title     = {Epistemic uncertainty-aware {Barlow Twins} reduced order modeling for nonlinear contact problems},
  journal   = {IEEE Access},
  year      = {2023},
  doi       = {10.1109/access.2023.3284837},
  url       = {https://doi.org/10.1109/access.2023.3284837},
}

@inproceedings{zeng2023bayesian,
  author    = {Zeng, X. and Geraci, G. and Gorodetsky, A. A. and Jakeman, J. D. and Eldred, M. S. and Ghanem, R. G.},
  title     = {Improving {Bayesian} networks multifidelity surrogate construction with basis adaptation},
  booktitle = {AIAA SciTech Forum},
  year      = {2023},
  doi       = {10.2514/6.2023-0917},
  url       = {https://doi.org/10.2514/6.2023-0917},
}

@inproceedings{thompson2023tuning,
  author    = {Thompson, M. and Geraci, G. and Bomarito, G. and Warner, J. and Leser, P. and Leser, W. P. and Eldred, M. S. and Jakeman, J. D. and Gorodetsky, A. A.},
  title     = {Strategies for automation of model tuning in multi-fidelity trajectory uncertainty propagation},
  booktitle = {AIAA SciTech Forum},
  year      = {2023},
  doi       = {10.2514/6.2023-1481},
  url       = {https://doi.org/10.2514/6.2023-1481},
}

@article{tezaur2022e3sm,
  author    = {Tezaur, I. and Peterson, K. and Powell, A. and Jakeman, J. D. and Roesler, E.},
  title     = {Global sensitivity analysis using the ultra-low resolution energy exascale {Earth System Model}},
  journal   = {Journal of Advances in Modeling Earth Systems},
  year      = {2022},
  volume    = {14},
  number    = {8},
  doi       = {10.1029/2021ms002831},
  url       = {https://doi.org/10.1029/2021ms002831},
}

@article{jakeman2022adaptive,
  author    = {Jakeman, J. D. and Friedman, S. and Eldred, M. S. and Tamellini, L. and Gorodetsky, A. A. and Allaire, D. J.},
  title     = {Adaptive experimental design for multi-fidelity surrogate modeling of multi-disciplinary systems},
  journal   = {International Journal for Numerical Methods in Engineering},
  year      = {2022},
  volume    = {123},
  number    = {12},
  doi       = {10.1002/nme.6958},
  url       = {https://doi.org/10.1002/nme.6958},
}

@article{kouri2022risk,
  author    = {Kouri, D. P. and Jakeman, J. D. and Huerta, J. G.},
  title     = {Risk-adapted optimal experimental design},
  journal   = {SIAM/ASA Journal on Uncertainty Quantification},
  year      = {2022},
  volume    = {10},
  number    = {2},
  doi       = {10.1137/20M1357615},
  url       = {https://doi.org/10.1137/20M1357615},
}

@article{jakeman2022surrogate,
  author    = {Jakeman, J. D. and Kouri, D. P. and Huerta, J. G.},
  title     = {Surrogate modeling for efficiently, accurately and conservatively estimating measures of risk},
  journal   = {Reliability Engineering \& System Safety},
  year      = {2022},
  volume    = {221},
  pages     = {108280},
  doi       = {10.1016/j.ress.2021.108280},
  url       = {https://doi.org/10.1016/j.ress.2021.108280},
}

@article{wang2022predictive,
  author    = {Wang, Q. and Guillaume, J. H. A. and Jakeman, J. D. and Yang, T. and Iwanaga, T. and Croke, B. and others},
  title     = {Assessing the predictive impact of factor fixing with an adaptive uncertainty-based approach},
  journal   = {Environmental Modelling \& Software},
  year      = {2022},
  volume    = {148},
  pages     = {105290},
  doi       = {10.1016/j.envsoft.2021.105290},
  url       = {https://doi.org/10.1016/j.envsoft.2021.105290},
}

@article{gorodetsky2022tensor,
  author    = {Gorodetsky, A. and Safta, C. and Jakeman, J. D.},
  title     = {Reverse-mode differentiation in arbitrary tensor network format: with application to supervised learning},
  journal   = {Journal of Machine Learning Research},
  year      = {2022},
  volume    = {23},
  number    = {1},
  url       = {http://jmlr.org/papers/v23/21-0225.html},
}

@inproceedings{bomarito2022trajectory,
  author    = {Bomarito, G. F. and Geraci, G. and Warner, J. E. and Leser, P. E. and Leser, W. P. and Eldred, M. S. and Jakeman, J. D. and Gorodetsky, A. A.},
  title     = {Improving multi-model trajectory simulation estimators using model selection and tuning},
  booktitle = {AIAA SciTech Forum},
  year      = {2022},
  doi       = {10.2514/6.2022-1099},
  url       = {https://doi.org/10.2514/6.2022-1099},
}

@article{gorodetsky2021mfnets,
  author    = {Gorodetsky, A. A. and Jakeman, J. D. and Geraci, G.},
  title     = {{MFNets}: data efficient all-at-once learning of multifidelity surrogates as directed networks of information sources},
  journal   = {Computational Mechanics},
  year      = {2021},
  volume    = {68},
  pages     = {741--758},
  doi       = {10.1007/s00466-021-02042-0},
  url       = {https://doi.org/10.1007/s00466-021-02042-0},
}

@article{harbrecht2021cholesky,
  author    = {Harbrecht, H. and Jakeman, J. D. and Zaspel, P.},
  title     = {Cholesky-based experimental design for {Gaussian} process and kernel-based emulation and calibration},
  journal   = {Communications in Computational Physics},
  year      = {2021},
  volume    = {29},
  number    = {4},
  doi       = {10.4208/cicp.oa-2020-0060},
  url       = {https://doi.org/10.4208/cicp.oa-2020-0060},
}

@article{razavi2021sensitivity,
  author    = {Razavi, S. and Jakeman, A. and Saltelli, A. and Prieur, C. and Iooss, B. and Borgonovo, E. and others},
  title     = {The future of sensitivity analysis: an essential discipline for systems modeling and policy support},
  journal   = {Environmental Modelling \& Software},
  year      = {2021},
  volume    = {137},
  pages     = {104954},
  doi       = {10.1016/j.envsoft.2020.104954},
  url       = {https://doi.org/10.1016/j.envsoft.2020.104954},
}

@article{qin2021nonautonomous,
  author    = {Qin, T. and Chen, Z. and Jakeman, J. D. and Xiu, D.},
  title     = {Data-driven learning of nonautonomous systems},
  journal   = {SIAM Journal on Scientific Computing},
  year      = {2021},
  volume    = {43},
  number    = {3},
  doi       = {10.1137/20m1342859},
  url       = {https://doi.org/10.1137/20m1342859},
}

@article{qin2021deeplearning,
  author    = {Qin, T. and Chen, Z. and Jakeman, J. D. and Xiu, D.},
  title     = {Deep learning of parameterized equations with applications to uncertainty quantification},
  journal   = {International Journal for Uncertainty Quantification},
  year      = {2021},
  volume    = {11},
  number    = {2},
  doi       = {10.1615/Int.J.UncertaintyQuantification.2020034123},
  url       = {https://doi.org/10.1615/Int.J.UncertaintyQuantification.2020034123},
}

@techreport{buluc2021rasc,
  author    = {Bulu\c{c}, A. and Kolda, T. G. and Wild, S. M. and Anitescu, M. and DeGennaro, A. and Jakeman, J. D. and others},
  title     = {Randomized algorithms for scientific computing ({RASC})},
  institution = {DOE ASCR Workshop Report},
  year      = {2021},
  doi       = {10.2172/1807223},
  url       = {https://doi.org/10.2172/1807223},
  eprint    = {2104.11079},
  archiveprefix = {arXiv},
}

@article{swiler2020constrained,
  author    = {Swiler, L. P. and Gulian, M. and Frankel, A. L. and Safta, C. and Jakeman, J. D.},
  title     = {A survey of constrained {Gaussian} process regression: approaches and implementation challenges},
  journal   = {Journal of Machine Learning for Modeling and Computing},
  year      = {2020},
  volume    = {1},
  number    = {2},
  doi       = {10.1615/JMachLearnModelComput.2020035155},
  url       = {https://doi.org/10.1615/JMachLearnModelComput.2020035155},
}

@article{gorodetsky2020acv,
  author    = {Gorodetsky, A. A. and Geraci, G. and Eldred, M. S. and Jakeman, J. D.},
  title     = {A generalized approximate control variate framework for multifidelity uncertainty quantification},
  journal   = {Journal of Computational Physics},
  year      = {2020},
  volume    = {408},
  pages     = {109257},
  doi       = {10.1016/j.jcp.2020.109257},
  url       = {https://doi.org/10.1016/j.jcp.2020.109257},
}

@article{jakeman2020adaptive,
  author    = {Jakeman, J. D. and Eldred, M. S. and Geraci, G. and Gorodetsky, A.},
  title     = {Adaptive multi-index collocation for uncertainty quantification and sensitivity analysis},
  journal   = {International Journal for Numerical Methods in Engineering},
  year      = {2020},
  volume    = {121},
  pages     = {1314--1343},
  doi       = {10.1002/nme.6268},
  url       = {https://doi.org/10.1002/nme.6268},
}

@article{gorodetsky2020mfnets,
  author    = {Gorodetsky, A. A. and Jakeman, J. D. and Geraci, G. and Eldred, M. S.},
  title     = {{MFNets}: multi-fidelity data-driven networks for {Bayesian} learning and prediction},
  journal   = {International Journal for Uncertainty Quantification},
  year      = {2020},
  volume    = {10},
  number    = {6},
  doi       = {10.1615/Int.J.UncertaintyQuantification.2020032978},
  url       = {https://doi.org/10.1615/Int.J.UncertaintyQuantification.2020032978},
}

@article{fu2020waterquality,
  author    = {Fu, B. and Horsburgh, J. S. and Jakeman, A. J. and Gualtieri, C. and Arnold, T. and Marshall, L. and Jakeman, J. D. and others},
  title     = {Modeling water quality in watersheds: from here to the next generation},
  journal   = {Water Resources Research},
  year      = {2020},
  volume    = {56},
  number    = {11},
  doi       = {10.1029/2020WR027721},
  url       = {https://doi.org/10.1029/2020WR027721},
}

@article{butler2020oed,
  author    = {Butler, T. and Jakeman, J. D. and Wildey, T.},
  title     = {Optimal experimental design for prediction based on push-forward probability measures},
  journal   = {Journal of Computational Physics},
  year      = {2020},
  volume    = {416},
  pages     = {109518},
  doi       = {10.1016/j.jcp.2020.109518},
  url       = {https://doi.org/10.1016/j.jcp.2020.109518},
}

@article{lozanovski2020lattice,
  author    = {Lozanovski, B. and Downing, D. and Tino, R. and Du Plessis, A. and Tran, P. and Jakeman, J. D. and others},
  title     = {Non-destructive simulation of node defects in additively manufactured lattice structures},
  journal   = {Additive Manufacturing},
  year      = {2020},
  volume    = {36},
  pages     = {101593},
  doi       = {10.1016/j.addma.2020.101593},
  url       = {https://doi.org/10.1016/j.addma.2020.101593},
}

@article{guillaume2019identifiability,
  author    = {Guillaume, J. H. A. and Jakeman, J. D. and Marsili-Libelli, S. and Asher, M. and Brunner, P. and others},
  title     = {Introductory overview of identifiability analysis: a guide to evaluating whether you have the right type of data for your modeling purpose},
  journal   = {Environmental Modelling \& Software},
  year      = {2019},
  volume    = {119},
  pages     = {418--432},
  doi       = {10.1016/j.envsoft.2019.07.007},
  url       = {https://doi.org/10.1016/j.envsoft.2019.07.007},
}

@article{jakeman2019pce,
  author    = {Jakeman, J. D. and Franzelin, F. and Narayan, A. and Eldred, M. and Pfl\"uger, D.},
  title     = {Polynomial chaos expansions for dependent random variables},
  journal   = {Computer Methods in Applied Mechanics and Engineering},
  year      = {2019},
  volume    = {351},
  pages     = {643--666},
  doi       = {10.1016/j.cma.2019.03.049},
  url       = {https://doi.org/10.1016/j.cma.2019.03.049},
}

@inproceedings{geraci2019sequoia,
  author    = {Geraci, G. and Eldred, M. S. and Gorodetsky, A. A. and Jakeman, J. D.},
  title     = {Recent advancements in multilevel-multifidelity techniques for forward {UQ} in the {DARPA} Sequoia project},
  booktitle = {AIAA SciTech Forum},
  year      = {2019},
  doi       = {10.2514/6.2019-0722},
  url       = {https://doi.org/10.2514/6.2019-0722},
}

@article{butler2018pushforward,
  author    = {Butler, T. and Jakeman, J. D. and Wildey, T.},
  title     = {Combining push-forward measures and {Bayes}' rule to construct consistent solutions to stochastic inverse problems},
  journal   = {SIAM Journal on Scientific Computing},
  year      = {2018},
  volume    = {40},
  number    = {2},
  pages     = {A984--A1011},
  doi       = {10.1137/16M1087229},
  url       = {https://doi.org/10.1137/16M1087229},
}

@article{butler2018convergence,
  author    = {Butler, T. and Jakeman, J. D. and Wildey, T.},
  title     = {Convergence of probability densities using approximate models for forward and inverse problems in uncertainty quantification},
  journal   = {SIAM Journal on Scientific Computing},
  year      = {2018},
  volume    = {40},
  number    = {5},
  pages     = {A3523--A3548},
  doi       = {10.1137/18M1181675},
  url       = {https://doi.org/10.1137/18M1181675},
}

@article{gorodetsky2018tensortrain,
  author    = {Gorodetsky, A. A. and Jakeman, J. D.},
  title     = {Gradient-based optimization for regression in the functional tensor-train format},
  journal   = {Journal of Computational Physics},
  year      = {2018},
  volume    = {374},
  pages     = {1219--1238},
  doi       = {10.1016/j.jcp.2018.08.010},
  url       = {https://doi.org/10.1016/j.jcp.2018.08.010},
}

@article{jakeman2018quadrature,
  author    = {Jakeman, J. D. and Narayan, A.},
  title     = {Generation and application of multivariate polynomial quadrature rules},
  journal   = {Computer Methods in Applied Mechanics and Engineering},
  year      = {2018},
  volume    = {338},
  pages     = {134--161},
  doi       = {10.1016/j.cma.2018.04.009},
  url       = {https://doi.org/10.1016/j.cma.2018.04.009},
}

@article{walsh2018oed,
  author    = {Walsh, S. N. and Wildey, T. M. and Jakeman, J. D.},
  title     = {Optimal experimental design using a consistent {Bayesian} approach},
  journal   = {ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B},
  year      = {2018},
  volume    = {4},
  number    = {1},
  doi       = {10.1115/1.4037457},
  url       = {https://doi.org/10.1115/1.4037457},
}

@article{adcock2018compressed,
  author    = {Adcock, B. and Bao, A. and Jakeman, J. D. and Narayan, A.},
  title     = {Compressed sensing with sparse corruptions: fault-tolerant sparse collocation approximations},
  journal   = {SIAM/ASA Journal on Uncertainty Quantification},
  year      = {2018},
  volume    = {6},
  number    = {4},
  pages     = {1424--1453},
  doi       = {10.1137/17M112590X},
  url       = {https://doi.org/10.1137/17M112590X},
}

@article{jakeman2018basis,
  author    = {Jakeman, J. D. and Pulch, R.},
  title     = {Time and frequency domain methods for basis selection in random linear dynamical systems},
  journal   = {International Journal for Uncertainty Quantification},
  year      = {2018},
  volume    = {8},
  number    = {6},
  doi       = {10.1615/Int.J.UncertaintyQuantification.2018026902},
  url       = {https://doi.org/10.1615/Int.J.UncertaintyQuantification.2018026902},
}

@inproceedings{eldred2018sequoia,
  author    = {Eldred, M. S. and Geraci, G. and Gorodetsky, A. and Jakeman, J. D.},
  title     = {Multilevel-multifidelity approaches for forward {UQ} in the {DARPA} {SEQUOIA} project},
  booktitle = {AIAA SciTech Forum},
  year      = {2018},
  doi       = {10.2514/6.2018-1179},
  url       = {https://doi.org/10.2514/6.2018-1179},
}

@article{jakeman2017sampling,
  author    = {Jakeman, J. D. and Narayan, A. and Zhou, T.},
  title     = {A generalized sampling and preconditioning scheme for sparse approximation of polynomial chaos expansions},
  journal   = {SIAM Journal on Scientific Computing},
  year      = {2017},
  volume    = {39},
  number    = {3},
  pages     = {A1114--A1144},
  doi       = {10.1137/16M1063885},
  url       = {https://doi.org/10.1137/16M1063885},
}

@article{narayan2017christoffel,
  author    = {Narayan, A. and Jakeman, J. D. and Zhou, T.},
  title     = {A {Christoffel} function weighted least squares algorithm for collocation approximations},
  journal   = {Mathematics of Computation},
  year      = {2017},
  volume    = {86},
  number    = {306},
  pages     = {1913--1947},
  doi       = {10.1090/mcom/3192},
  url       = {https://doi.org/10.1090/mcom/3192},
}

@inproceedings{alonso2017sequoia,
  author    = {Alonso, J. J. and Eldred, M. S. and Constantine, P. and Duraisamy, K. and Farhat, C. and Iaccarino, G. and Jakeman, J. D. and others},
  title     = {Scalable environment for quantification of uncertainty and optimization in industrial applications ({SEQUOIA})},
  booktitle = {AIAA SciTech Forum},
  year      = {2017},
  doi       = {10.2514/6.2017-1327},
  url       = {https://doi.org/10.2514/6.2017-1327},
}

@article{jakeman2015l1,
  author    = {Jakeman, J. D. and Eldred, M. S. and Sargsyan, K.},
  title     = {Enhancing $\ell_1$-minimization estimates of polynomial chaos expansions using basis selection},
  journal   = {Journal of Computational Physics},
  year      = {2015},
  volume    = {289},
  pages     = {18--34},
  doi       = {10.1016/j.jcp.2015.02.025},
  url       = {https://doi.org/10.1016/j.jcp.2015.02.025},
}

@article{chen2015local,
  author    = {Chen, Y. and Jakeman, J. D. and Gittelson, C. and Xiu, D.},
  title     = {Local polynomial chaos expansion for linear differential equations with high-dimensional random inputs},
  journal   = {SIAM Journal on Scientific Computing},
  year      = {2015},
  volume    = {37},
  number    = {1},
  pages     = {A79--A102},
  doi       = {10.1137/140970100},
  url       = {https://doi.org/10.1137/140970100},
}

@article{jakeman2015sparse,
  author    = {Jakeman, J. D. and Wildey, T.},
  title     = {Enhancing adaptive sparse grid approximations and improving refinement strategies using adjoint-based a posteriori error estimates},
  journal   = {Journal of Computational Physics},
  year      = {2015},
  volume    = {280},
  pages     = {54--71},
  doi       = {10.1016/j.jcp.2014.09.014},
  url       = {https://doi.org/10.1016/j.jcp.2014.09.014},
}

@article{narayan2014leja,
  author    = {Narayan, A. and Jakeman, J. D.},
  title     = {Adaptive {Leja} sparse grid constructions for stochastic collocation and high-dimensional approximation},
  journal   = {SIAM Journal on Scientific Computing},
  year      = {2014},
  volume    = {36},
  number    = {6},
  pages     = {A2952--A2983},
  doi       = {10.1137/140966368},
  url       = {https://doi.org/10.1137/140966368},
}

@incollection{jakeman2013sparsegrids,
  author    = {Jakeman, J. D. and Roberts, S. G.},
  title     = {Local and dimension adaptive stochastic collocation for uncertainty quantification},
  booktitle = {Sparse Grids and Applications},
  series    = {Lecture Notes in Computational Science and Engineering},
  volume    = {88},
  pages     = {181--203},
  publisher = {Springer Berlin Heidelberg},
  year      = {2013},
  doi       = {10.1007/978-3-642-31703-3_9},
  url       = {https://doi.org/10.1007/978-3-642-31703-3_9},
}

@article{jakeman2013multielement,
  author    = {Jakeman, J. D. and Narayan, A. and Xiu, D.},
  title     = {Minimal multi-element stochastic collocation for uncertainty quantification of discontinuous functions},
  journal   = {Journal of Computational Physics},
  year      = {2013},
  volume    = {242},
  pages     = {790--811},
  doi       = {10.1016/j.jcp.2013.02.035},
  url       = {https://doi.org/10.1016/j.jcp.2013.02.035},
}

@article{jakeman2011discontinuities,
  author    = {Jakeman, J. D. and Archibald, R. and Xiu, D.},
  title     = {Characterization of discontinuities in high-dimensional stochastic problems on adaptive sparse grids},
  journal   = {Journal of Computational Physics},
  year      = {2011},
  volume    = {230},
  number    = {10},
  pages     = {3977--3997},
  doi       = {10.1016/j.jcp.2011.02.022},
  url       = {https://doi.org/10.1016/j.jcp.2011.02.022},
}

@article{jakeman2010epistemic,
  author    = {Jakeman, J. D. and Eldred, M. and Xiu, D.},
  title     = {Numerical approach for quantification of epistemic uncertainty},
  journal   = {Journal of Computational Physics},
  year      = {2010},
  volume    = {229},
  number    = {12},
  pages     = {4648--4663},
  doi       = {10.1016/j.jcp.2010.03.003},
  url       = {https://doi.org/10.1016/j.jcp.2010.03.003},
}

@article{jakeman2010tsunami,
  author    = {Jakeman, J. D. and Nielsen, O. M. and Putten, K. V. and Mleczko, R. and Burbidge, D. and Horspool, N.},
  title     = {Towards spatially distributed quantitative assessment of tsunami inundation models},
  journal   = {Ocean Dynamics},
  year      = {2010},
  volume    = {60},
  pages     = {1115--1138},
  doi       = {10.1007/s10236-010-0312-4},
  url       = {https://doi.org/10.1007/s10236-010-0312-4},
}