John Davis Jakeman

Optimization & Uncertainty Quantification

Author profile picture

Optimization & Uncertainty Quantification

jdjakem@sandia.gov

(505) 284-9097

Sandia National Laboratories, New Mexico
 P.O. Box 5800
 Albuquerque, NM 87185-1318

Biography

I specialize in developing and utilizing machine learning methods for credible data-informed decision making. My experience lies in the intersection of mathematics, statistics and computer science. I am the founding developer of PyApprox which is a Python toolbox for machine learning, uncertainty quantification and design of experiments. I am a leader in making predictions and decisions using data of varying credibility and cost and optimally allocating resources to minimize error subject to budgetary constraints.

Algorithmic Advances

Credible making decisions under uncertainty requires a multi-disciplinary team and the development and tailoring algorithms to the individual challenges of a given application. Consequently, my research portfolio is very broad and includes the development of novel methods associated with:

  • Machine learning: multi-fidelity information fusion; low-rank tensor-decomposition; Gaussian processes; polynomial chaos expansions; sparse-grids; risk-adverse regression; compressed sensing.
  • Probabilistic inverse problems: Bayesian inference; push-forward based inference.
  • Experimental design: optimal design of computer experiments for interpolation regression and compressed sensing; risk-adverse optimal experimental design

Application Advances

I am enthusiastic about using fundamental theoretical and algorithmic advances to help address the complex challenges faced by simulation aided decision making. Areas I have or am currently working on include:

  • Engineering: direct field acoustic testing; additive manufacturing of lattices; design of aerospace nozzles.
  • Climate: ice-sheet evolution; arctic sea-ice evolution
  • Plasma physics: high-density fusion

Reproducible and Maintainable Software

I believe developing modular, easy to software that is simple to develop and maintain is essential for addressing the continually evolving challenges faced by high-consequence decision making. These principles are reflected in the Python toolbox PyApprox whose development I lead. PyApprox is also accompanied by an extensive set of documentation, including tutorials and examples, that aim to improve the accessibility of machine learning methods for credible data-informed decision making.

Education

  • B.Sc. Mathematics. (Honours 1). Australian National University, 2003-2006.
  • Ph.D. Mathematics. Australian National University, 2007-2011.
  • Postdoctoral associate. Purdue University, 2011.
  • Postdoctoral associate. Statistical and Applied Mathematical Sciences Institute (SAMSI), 2011.
  • Postdoctoral associate. Sandia National Laboratories, 2012-2014.

Publications

  • Jakeman, J., Eldred, M., Geraci, G., Seidl, D., Smith, T., Gorodetsky, A., Pham, T., Narayan, A., Zeng, X., Ghanem, R., & Ghanem, R. (2022). Multi-fidelity information fusion and resource allocation. https://doi.org/10.2172/1888363 Publication ID: 80245
  • Tezaur, I., Peterson, K., Powell, A., Jakeman, J., Roesler, E., & Roesler, E. (2022). Global Sensitivity Analysis Using the Ultra‐Low Resolution Energy Exascale Earth System Model. Journal of Advances in Modeling Earth Systems, 14(8). https://doi.org/10.1029/2021MS002831 Publication ID: 80057
  • Jakeman, J. (2022). PyApprox: Enabling efficient model analysis. https://doi.org/10.2172/1879614 Publication ID: 80040
  • Jakeman, J., Friedman, S., Eldred, M., Tamellini, L., Gorodetsky, A.A., Allaire, D., & Allaire, D. (2022). Adaptive experimental design for multi-fidelity surrogate modeling of multi-disciplinary systems. International Journal for Numerical Methods in Engineering, 123(12), pp. 2760-2790. https://doi.org/10.1002/nme.6958 Publication ID: 80511
  • Jakeman, J., Kouri, D.P., Huerta, J., & Huerta, J. (2022). Surrogate modeling for efficiently, accurately and conservatively estimating measures of risk. Reliability Engineering and System Safety, 221. https://doi.org/10.1016/j.ress.2021.108280 Publication ID: 80231
  • Wang, Q., Guillaume, J.H.A., Jakeman, J., Yang, T., Iwanaga, T., Croke, B., Jakeman, A.J., & Jakeman, A.J. (2022). Assessing the predictive impact of factor fixing with an adaptive uncertainty-based approach. Environmental Modelling and Software, 148. https://doi.org/10.1016/j.envsoft.2021.105290 Publication ID: 80234
  • Gorodetsky, A.A., Safta, C., Jakeman, J., & Jakeman, J. (2022). Reverse-mode differentiation in arbitrary tensor network format: with application to supervised learning. Journal of Machine Learning Research, 23. https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85131439706&origin=inward Publication ID: 80727
  • Eldred, M., Geraci, G., Gorodetsky, A., Jakeman, J., Portone, T., Wildey, T., Rushdi, A.A., Seidl, D., & Seidl, D. (2021). The Dakota Project: Connecting the Pipeline from Uncertainty Quantification R&D to Mission Impact [Presentation]. https://www.osti.gov/biblio/1891078 Publication ID: 76127
  • Kouri, D.P., Jakeman, J., Huerta, J., Walsh, T., Smith, C., Uryasev, S., & Uryasev, S. (2021). Risk-Adaptive Experimental Design for High-Consequence Systems: LDRD Final Report. https://doi.org/10.2172/1820307 Publication ID: 75666
  • White, R.D., Jakeman, J., van Bloemen Waanders, B., Kouri, D.P., Alexanderian, A., & Alexanderian, A. (2021). Exploring risk-averse design criteria for sequential optimal experimental design in a Bayesian setting [Conference Presenation]. https://doi.org/10.2172/1888463 Publication ID: 75823
  • Morrow, Z., van Bloemen Waanders, B., Jakeman, J., & Jakeman, J. (2021). Characterizing Approximation Methods for Digital Twins in Scientific Computing [Conference Presenation]. https://doi.org/10.2172/1889008 Publication ID: 75870
  • Seidl, D., Jakeman, J., & Jakeman, J. (2021). Improving Digital Twins by Learning from a Fleet of Assets [Conference Presenation]. https://doi.org/10.2172/1889023 Publication ID: 75878
  • Jakeman, J., Kouri, D.P., Huerta, J., & Huerta, J. (2021). Surrogate Modeling For Efficiently, Accurately and Conservatively Estimating Measures of Risk [Conference Presenation]. https://doi.org/10.2172/1889571 Publication ID: 75892
  • Friedman, S., Jakeman, J., Eldred, M., Tamellini, L., Gorodestky, A., Allaire, D., & Allaire, D. (2021). Adaptive resource allocation for surrogate modeling of systems comprised of multiple disciplines with varying fidelity. https://doi.org/10.2172/1807453 Publication ID: 78769
  • Jakeman, J., Kouri, D.P., Huerta, J., & Huerta, J. (2021). Surrogate Modeling For Efficiently Accurately and Conservatively Estimating Measures of Risk. https://doi.org/10.2172/1807455 Publication ID: 78808
  • Jakeman, J., Eldred, M., Geraci, G., Portone, T., Rushdi, A.A., Seidl, D., Smith, T., & Smith, T. (2021). Multi-fidelity Machine Learning [Conference Presenation]. https://doi.org/10.2172/1876608 Publication ID: 79162
  • Zeng, X., Geraci, G., Eldred, M., Jakeman, J., Gorodetsky, A., Ghanem, R., & Ghanem, R. (2021). Adaptive Basis for Multifidelity Uncertainty Quantification [Conference Presenation]. https://doi.org/10.2172/1889016 Publication ID: 79504
  • Eldred, M., Geraci, G., Gorodetsky, A., Jakeman, J., Portone, T., & Portone, T. (2021). Efficient Deployment of Multifidelity Sampling Methods in Production Settings [Conference Presenation]. https://doi.org/10.2172/1882491 Publication ID: 79508
  • Wildey, T., Butler, T., Jakeman, J., Tran, A., & Tran, A. (2021). Solving Stochastic Inverse Problems for Property-Structure Relationships in Computational Materials Science [Conference Presenation]. https://doi.org/10.2172/1890916 Publication ID: 79535
  • Jakeman, J., Friedman, S., Eldred, M., Tamellini, L., Gorodetsky, A., Allaire, D., & Allaire, D. (2021). Adaptive resource allocation for surrogate modeling of systems comprised of multiple disciplines with varying fidelity [Conference Presenation]. https://doi.org/10.2172/1872879 Publication ID: 78820
  • Qin, T., Chen, Z., Jakeman, J., Xiu, D., & Xiu, D. (2021). Data-driven learning of nonautonomous systems. SIAM Journal on Scientific Computing, 43(3), pp. A1607-A1624. https://doi.org/10.1137/20m1342859 Publication ID: 75804
  • Reese, W., Hart, J., van Bloemen Waanders, B., Perergo, M., Jakeman, J., Saibaba, A., & Saibaba, A. (2021). Bedrock Inversion and Hyper Differential Sensitivity Analysis for the Shallow Ice Model [Presentation]. https://www.osti.gov/biblio/1889590 Publication ID: 78605
  • Harbrecht, H., Jakeman, J., Zaspel, P., & Zaspel, P. (2021). Cholesky-based experimental design for gaussian process and kernel-based emulation and calibration. Communications in Computational Physics, 29(4), pp. 1152-1185. https://doi.org/10.4208/cicp.OA-2020-0060 Publication ID: 71546
  • Wildey, T., Butler, T., Jakeman, J., & Jakeman, J. (2021). Combining Measure Theory and Bayes? Rule to Solve a Stochastic Inverse Problem [Conference Presenation]. https://doi.org/10.2172/1877851 Publication ID: 78143
  • Razavi, S., Jakeman, A., Saltelli, A., Prieur, C., Iooss, B., Borgonovo, E., Plischke, E., Lo Piano, S., Iwanaga, T., Becker, W., Tarantola, S., Guillaume, J.H.A., Jakeman, J., Gupta, H., Melillo, N., Rabitti, G., Chabridon, V., Duan, Q., Sun, X., … Maier, H.R. (2021). The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support. Environmental Modelling and Software, 137. https://doi.org/10.1016/j.envsoft.2020.104954 Publication ID: 74992
  • Safta, C., Sargsyan, K., Jakeman, J., Gorodetsky, A., & Gorodetsky, A. (2021). Low-Rank Tensor Network Approximations for Earth System Model [Conference Presenation]. https://doi.org/10.2172/1854317 Publication ID: 77458
  • Jakeman, J., Gorodetsky, A., Eldred, M., Geraci, G., Smith, T., & Smith, T. (2021). MFNETS: Multi-Fidelity Data-Driven Networks for Data Analysis [Conference Presenation]. https://doi.org/10.2172/1854429 Publication ID: 77472
  • Swiler, L., Gulian, M., Frankel, A., Safta, C., Jakeman, J., & Jakeman, J. (2021). Constrained Gaussian Processes: A Survey [Conference Presenation]. https://doi.org/10.2172/1847480 Publication ID: 77280
  • Eldred, M., Gorodetsky, A., Geraci, G., Jakeman, J., Portone, T., & Portone, T. (2021). Recent Advances in Adaptive Refinement of (Regression-Based) Multifidelity Surrogates for UQ [Conference Presenation]. https://doi.org/10.2172/1847573 Publication ID: 77372
  • Debusschere, B., Geraci, G., Jakeman, J., Safta, C., Swiler, L., & Swiler, L. (2021). Polynomial Chaos Expansions for Discrete Random Variables in Cyber Security Emulytics Experiments [Conference Presenation]. https://doi.org/10.2172/1847628 Publication ID: 77383
  • Qin, T., Chen, Z., Jakeman, J., Xiu, D., & Xiu, D. (2021). Deep learning of parameterized equations with applications to uncertainty quantification. International Journal for Uncertainty Quantification, 11(2), pp. 63-82. https://doi.org/10.1615/int.j.uncertaintyquantification.2020034123 Publication ID: 71096
  • Gorodetsky, A., Tsuji, K., Jakeman, J., Geraci, G., Eldred, M., & Eldred, M. (2020). Multifidelity information fusion via network models for uncertainty quantification in aerospace dynamical systems [Conference Presenation]. https://doi.org/10.2172/1836910 Publication ID: 72268
  • Lozanovski, B., Downing, D., Tino, R., du Plessis, A., Tran, P., Jakeman, J., Shidid, D., Emmelmann, C., Qian, M., Choong, P., Brandt, M., Leary, M., & Leary, M. (2020). Non-destructive simulation of node defects in additively manufactured lattice structures. Additive Manufacturing, 36. https://doi.org/10.1016/j.addma.2020.101593 Publication ID: 74924
  • Geraci, G., Eldred, M., Gorodetsky, A., Jakeman, J., & Jakeman, J. (2020). Multifidelity Strategies in UQ: an overview on some recent trends in sampling based approaches [Conference Poster]. https://www.osti.gov/biblio/1822111 Publication ID: 74972
  • Gulian, M., Swiler, L., Frankel, A., Safta, C., Jakeman, J., & Jakeman, J. (2020). A Survey of Constrained Gaussian Process Regression: Approaches and Implementation Challenges [Conference Poster]. https://www.osti.gov/biblio/1814448 Publication ID: 74592
  • Gulian, M., Swiler, L., Frankel, A., Jakeman, J., Safta, C., & Safta, C. (2020). A Survey of Constrained Gaussian Process Regression: Approaches and Implementation Challenges [Conference Poster]. https://www.osti.gov/biblio/1812282 Publication ID: 74359
  • Gorodetsky, A.A., Geraci, G., Eldred, M., Jakeman, J., & Jakeman, J. (2020). A generalized approximate control variate framework for multifidelity uncertainty quantification. Journal of Computational Physics, 408. https://doi.org/10.1016/j.jcp.2020.109257 Publication ID: 71027
  • Jakeman, J., Eldred, M.S., Geraci, G., Gorodetsky, A., & Gorodetsky, A. (2020). Adaptive multi-index collocation for uncertainty quantification and sensitivity analysis. International Journal for Numerical Methods in Engineering. https://doi.org/10.2172/1574406 Publication ID: 66292
  • Gorodetsky, A.A., Jakeman, J., Geraci, G., Eldred, M., & Eldred, M. (2020). Mfnets: Multi-fidelity data-driven networks for bayesian learning and prediction. International Journal for Uncertainty Quantification, 10(6), pp. 595-622. https://doi.org/10.1615/Int.J.UncertaintyQuantification.2020032978 Publication ID: 74093
  • Safta, C., Sargsyan, K., Jakeman, J., & Jakeman, J. (2019). Uncertainty Quantification for E3SM Land Component using Low-Rank Surrogate Models [Conference Poster]. https://www.osti.gov/biblio/1643449 Publication ID: 66773
  • Wildey, T., Bruder, L., Bui-Thanh, T., Butler, T., Jakeman, J., Marvin, B., Tran, A., Walsh, S., & Walsh, S. (2019). Moving Beyond Forward Simulation to Enable Data-informed Physics-based Predictions [Presentation]. https://www.osti.gov/biblio/1646273 Publication ID: 66318
  • Jakeman, J. (2019). Uncertainty Quantification: An Overview [Conference Poster]. https://www.osti.gov/biblio/1643293 Publication ID: 66341
  • Geraci, G., Eldred, M., Gorodetsky, A., Jakeman, J., & Jakeman, J. (2019). Recent advancement in Multifidelity Uncertainty Quantification [Conference Poster]. https://www.osti.gov/biblio/1642820 Publication ID: 65621
  • Wildey, T., Butler, T., Jakeman, J., & Jakeman, J. (2019). Convergence of Probability Densities using Approximate Models for Forward and Inverse Problems in Uncertainty Quantification [Conference Poster]. https://www.osti.gov/biblio/1641989 Publication ID: 64879
  • Eldred, M., Geraci, G., Gorodetsky, A., Jakeman, J., & Jakeman, J. (2019). Multilevel / Multifidelity Sampling and Emulation for Forward UQ [Presentation]. https://www.osti.gov/biblio/1645988 Publication ID: 65019
  • Jakeman, J., Franzelin, F., Narayan, A., Eldred, M., Plfüger, D., & Plfüger, D. (2019). Polynomial chaos expansions for dependent random variables [Conference Poster]. Computer Methods in Applied Mechanics and Engineering. https://doi.org/10.1016/j.cma.2019.03.049 Publication ID: 63130
  • Wildey, T., Butler, T., Jakeman, J., Bruder, L., & Bruder, L. (2019). Solving Stochastic Inverse Problems using Approximate Push-forward Densities based on a Multi-fidelity Monte Carlo Method [Conference Poster]. https://www.osti.gov/biblio/1641047 Publication ID: 69562
  • Casey, T., Debusschere, B., Eldred, M., Geraci, G., Ghanem, R., Jakeman, J., Marzouk, Y., Najm, H.N., Safta, C., Sargsyan, K., & Sargsyan, K. (2019). FASTMath: UQ Algorithms [Conference Poster]. https://www.osti.gov/biblio/1641088 Publication ID: 69621
  • Safta, C., Reid, T., Jakeman, J., Sargsyan, K., & Sargsyan, K. (2019). Approximating Data with Stochastic and Physical Dependence using the Functional Tensor Train Models [Conference Poster]. https://www.osti.gov/biblio/1641238 Publication ID: 69801
  • Eldred, M., Geraci, G., Gorodetsky, A., Jakeman, J., & Jakeman, J. (2019). Experience with Multilevel/Multifidelity/Multi-Index Sampling and Surrogate Approaches for Forward Uncertainty Quantification [Conference Poster]. https://www.osti.gov/biblio/1641388 Publication ID: 70119
  • Geraci, G., Eldred, M., Gorodetsky, A., Jakeman, J., & Jakeman, J. (2019). Recent Advancements for Multifidelity UQ and OUU in Dakota: Capability Overview and Perspectives [Conference Poster]. https://www.osti.gov/biblio/1641419 Publication ID: 70164
  • Safta, C., Jakeman, J., Gorodetsky, A., & Gorodetsky, A. (2019). Low-Rank Functional Tensor Train Representations for High-Dimensional Computational Models [Presentation]. https://www.osti.gov/biblio/1645344 Publication ID: 69010
  • Safta, C., Sargsyan, K., Jakeman, J., Gorodetsky, A., Ricciuto, D., & Ricciuto, D. (2019). Exploiting Model Structure for Forward Propagation of Uncertainty in Earth System Models [Conference Poster]. https://www.osti.gov/biblio/1640926 Publication ID: 69353
  • Jakeman, J. (2019). A mathematical perspective on the certification and design of physical systems in the presence of uncertainty [Presentation]. https://www.osti.gov/biblio/1645241 Publication ID: 68525
  • Bertagna, L., Jakeman, J., Perego, M., Tezaur, I., Watkins, J., Salinger, A., Asay-Davis, X., Hoffman, M., Price, S., Zhang, T., Stadler, G., & Stadler, G. (2019). Modeling Ice Sheets with MALI [Presentation]. https://www.osti.gov/biblio/1645332 Publication ID: 68862
  • Safta, C., Sargsyan, K., Jakeman, J., Gorodetsky, A., Ricciuto, D., & Ricciuto, D. (2019). Exploiting Low-Rank Structure for Sensitivity Analysis in Earth System Models [Conference Poster]. https://www.osti.gov/biblio/1639271 Publication ID: 67283
  • Geraci, G., Gorodetsky, A., Eldred, M., Jakeman, J., & Jakeman, J. (2019). Recent advancements toward generalized sampling strategies for multifidelity Uncertainty Quantification [Presentation]. https://www.osti.gov/biblio/1644568 Publication ID: 67615
  • Perego, M., Jakeman, J., Severa, W., Ruthotto, L., & Ruthotto, L. (2019). Neural Networks Surrogates of PDE-based Dynamical Systems: Application to Ice Sheet Dynamics [Conference Poster]. https://www.osti.gov/biblio/1639251 Publication ID: 67249
  • Geraci, G., Eldred, M., Gorodetsky, A., Jakeman, J., & Jakeman, J. (2019). Recent advancements in Multilevel-Multifidelity techniques for forward UQ in the DARPA Sequoia project [Conference Poster]. https://doi.org/10.2514/6.2019-0722 Publication ID: 64164
  • Wildey, T., Butler, T., Jakeman, J., Bui-Thanh, T., Marvin, B., Bruder, L., & Bruder, L. (2019). Developing Scalable and Multi-fidelity Approaches for Push-forward Based Inference [Conference Poster]. https://www.osti.gov/biblio/1596420 Publication ID: 64514
  • Gorodetsky, A.A., Jakeman, J., & Jakeman, J. (2018). Gradient-based optimization for regression in the functional tensor-train format. Journal of Computational Physics, 374, pp. 1219-1238. https://doi.org/10.1016/j.jcp.2018.08.010 Publication ID: 60350
  • Safta, C., Ricciuto, D., Gorodetsky, A., Jakeman, J., & Jakeman, J. (2018). Exploiting Model Structure for Global Sensitivity Analysis in E3SM Land Model [Conference Poster]. https://www.osti.gov/biblio/1761160 Publication ID: 60522
  • Wildey, T., Butler, T., Jakeman, J., & Jakeman, J. (2018). The Consistent Bayesian Approach for Stochastic Inverse Problems [Conference Poster]. https://www.osti.gov/biblio/1592669 Publication ID: 59876
  • Jakeman, J., Perego, M., Severa, W., & Severa, W. (2018). Neural Networks as Surrogates of Nonlinear High-Dimensional Parameter-to-Prediction Maps. https://doi.org/10.2172/1531317 Publication ID: 59302
  • Jakeman, J., Narayan, A., & Narayan, A. (2018). Generation and application of multivariate polynomial quadrature rules. Computer Methods in Applied Mechanics and Engineering, 338, pp. 134-161. https://doi.org/10.1016/j.cma.2018.04.009 Publication ID: 60352
  • Eldred, M., Geraci, G., Gorodetsky, A., Jakeman, J., & Jakeman, J. (2018). Lecture 1: Multilevel-Multifidelity with Monte Carlo Sampling; Algorithms and deployment experience [Presentation]. https://www.osti.gov/biblio/1582192 Publication ID: 63755
  • Wildey, T., Butler, T., Jakeman, J., Marvin, B., & Marvin, B. (2018). Consistent Bayesian Inference with Push-forward Measures: Scalable Implementations and Applications [Conference Poster]. https://www.osti.gov/biblio/1567819 Publication ID: 62972
  • Jakeman, J., Butler, T., Eldred, M., Geraci, G., Gorodetsky, A., Wildey, T., & Wildey, T. (2018). Adaptive multi-index collocation for quantifying uncertainty [Conference Poster]. https://www.osti.gov/biblio/1806541 Publication ID: 63211
  • Perego, M., Bertagna, L., Hoffman, M., Jakeman, J., Price, S., Salinger, A., Stadler, G., Tezaur, I., Watkins, J., & Watkins, J. (2018). Ice Sheet Modeling: Computational and Mathematical Challenges [Presentation]. https://www.osti.gov/biblio/1513472 Publication ID: 62055
  • Jakeman, J., Perego, M., Tezaur, I., Price, S., Stadler, G., & Stadler, G. (2018). Ice Sheet Initialization and Uncertainty Quantification of SeaLevel Rise [Conference Poster]. https://www.osti.gov/biblio/1523714 Publication ID: 62288
  • Wildey, T., Butler, T., Jakeman, J., Seidl, D., van Bloemen Waanders, B., & van Bloemen Waanders, B. (2018). Data-informed Multiscale Modeling of Additive Materials [Conference Poster]. https://www.osti.gov/biblio/1523778 Publication ID: 62297
  • Wildey, T., Butler, T., Jakeman, J., Walsh, S., & Walsh, S. (2018). Optimal Experimental Design for Prediction Using a Consistent Bayesian Approach [Conference Poster]. https://www.osti.gov/biblio/1507835 Publication ID: 61612
  • Adcock, B., Bao, A., Jakeman, J., Naryan, A., & Naryan, A. (2018). Compressed sensing with sparse corruptions: Fault-tolerant sparse collocation approximations. https://doi.org/10.2172/1434573 Publication ID: 61625
  • Eldred, M., Geraci, G., Gorodetsky, A., Jakeman, J., & Jakeman, J. (2018). Adaptive Refinement Strategies for Multilevel Polynomial Chaos Expansions [Conference Poster]. https://www.osti.gov/biblio/1575179 Publication ID: 61666
  • Perego, M., Jakeman, J., Perego, M., Tezaur, I., Price, S., Stadler, G., & Stadler, G. (2018). Methodologies for Enabling Bayesian Calibration in Landice Modeling Towards Probabilistic Projections of Sealevel Change [Conference Poster]. https://www.osti.gov/biblio/1510847 Publication ID: 61720
  • Geraci, G., Gorodetsky, A., Eldred, M., Jakeman, J., & Jakeman, J. (2018). TOWARDS LEVERAGING ACTIVE DIRECTION FOR EFFICIENT MULTIFIDELITY UQ STRATEGIES [Conference Poster]. https://www.osti.gov/biblio/1525631 Publication ID: 61779
  • Jakeman, A., Jakeman, J., & Jakeman, J. (2018). An overview of methods to identify and manage uncertainty for modelling problems in the water-environment-agriculture cross-sector. Mathematics for Industry, 28, pp. 147-171. https://doi.org/10.1007/978-981-10-7811-8_15 Publication ID: 58446
  • Walsh, S.N., Wildey, T., Jakeman, J., & Jakeman, J. (2018). Optimal experimental design using a consistent Bayesian approach. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 4(1). https://doi.org/10.1115/1.4037457 Publication ID: 56168
  • Safta, C., Jakeman, J., Ghanem, R., & Ghanem, R. (2018). Scalable Uncertainty Quantification: Exploiting Structure in Models and Data [Presentation]. https://www.osti.gov/biblio/1497534 Publication ID: 60773
  • Eldred, M., Geraci, G., Gorodetsky, A., Jakeman, J., & Jakeman, J. (2018). Multilevel-Multifidelity Approaches for Forward UQ in the DARPA SEQUOIA Project [Conference Poster]. https://www.osti.gov/biblio/1513488 Publication ID: 58681
  • Gorodetsky, A., Geraci, G., Eldred, M., Jakeman, J., & Jakeman, J. (2018). Multifidelity Model Management using Latent Variable Bayesian Networks [Conference Poster]. https://www.osti.gov/biblio/1513639 Publication ID: 58728
  • Peterson, K., Parks, M., Ackerman, E., Bambha, R.P., Bull, D., Frederick, J., Hardesty, J., Ilgen, A., Jakeman, J., Powell, A., Peterson, M., Roesler, E., Safta, C., Stracuzzi, D., Tezaur, I., & Tezaur, I. (2018). Arctic Tipping Points Triggering Global Change [Conference Poster]. https://www.osti.gov/biblio/1513640 Publication ID: 58729
  • Jakeman, J., Pulch, R., & Pulch, R. (2018). Time and Frequency Domain Methods for Basis Selection in Random Linear Dynamical Systems. International Journal for Uncertainty Quantification, 8(6), pp. 495-510. https://doi.org/10.1615/Int.J.UncertaintyQuantification.2018026902 Publication ID: 59010
  • Adcock, B., Bao, A., Jakeman, J., Narayan, A., & Narayan, A. (2018). Compressed sensing with sparse corruptions: Fault-tolerant sparse collocation approximations. SIAM-ASA Journal on Uncertainty Quantification, 6(4), pp. 1424-1453. https://doi.org/10.1137/17M112590X Publication ID: 59018
  • Jakeman, J., Perego, M., Tezaur, I., Price, S., & Price, S. (2017). Towards probabilistic predictions of future sea-level [Conference Poster]. https://www.osti.gov/biblio/1481488 Publication ID: 54018
  • Jakeman, J., Narayan, A., & Narayan, A. (2017). Generation and application of multivariate polynomial quadrature rules. https://doi.org/10.2172/1510651 Publication ID: 54017
  • Wildey, T., Butler, T., Jakeman, J., & Jakeman, J. (2017). A Consistent Bayesian Approach for Solving Stochastic Inverse Problems [Conference Poster]. https://www.osti.gov/biblio/1469097 Publication ID: 58335
  • Jakeman, J., Gorodetsky, A., Eldred, M., & Eldred, M. (2017). Tractable Uncertainty Quantification: Exploiting Structure [Conference Poster]. https://www.osti.gov/biblio/1466103 Publication ID: 58076
  • Wildey, T., Jakeman, J., Butler, T., & Butler, T. (2017). Advancing Beyond Interpretive Simulation to Inference for Prediction [Conference Poster]. https://www.osti.gov/biblio/1467988 Publication ID: 58203
  • Tezaur, I., Jakeman, J., Eldred, M., Perego, M., Price, S., Salinger, A., & Salinger, A. (2017). Large-scale Deterministic Inversion and Bayesian Calibration in Land-Ice Modeling [Conference Poster]. https://www.osti.gov/biblio/1460158 Publication ID: 57170
  • Geraci, G., Gorodetsky, A., Jakeman, J., Eldred, M., & Eldred, M. (2017). Sampling Polynomial Chaos and Functional Tensor Train Multilevel/Multifidelity Strategies for Forward UQ [Conference Poster]. https://www.osti.gov/biblio/1507076 Publication ID: 57348
  • Eldred, M., Geraci, G., Gorodetsky, A., Jakeman, J., & Jakeman, J. (2017). Multilevel-Multifidelity Expansions with Application to Forward UQ OUU and Emulator-Based Bayesian Inference [Conference Poster]. https://www.osti.gov/biblio/1507501 Publication ID: 57417
  • Eldred, M., Monschke, J., Jakeman, J., Geraci, G., & Geraci, G. (2017). Multilevel-Multifidelity Approaches for Uncertainty Quantification and Design [Conference Poster]. https://www.osti.gov/biblio/1455372 Publication ID: 56819
  • Gorodetsky, A., Jakeman, J., & Jakeman, J. (2017). High-dimensional regression of low-rank functions [Conference Poster]. https://www.osti.gov/biblio/1426383 Publication ID: 55240
  • Jakeman, J. (2017). Multivariate Quadrature Rules for Correlated Random Variables [Conference Poster]. https://www.osti.gov/biblio/1427962 Publication ID: 55379
  • Wildey, T., Jakeman, J., Butler, T., & Butler, T. (2017). Efficient Sampling Strategies for the Consistent Bayesian Approach for Solving Stochastic Inverse Problems [Conference Poster]. https://www.osti.gov/biblio/1425298 Publication ID: 55046
  • Bao, A., Adcock, B., Jakeman, J., Narayan, A., & Narayan, A. (2017). Compressive Sampling in Multivariate Polynomial Approximation with Corrupted Simulation Samples [Conference Poster]. https://www.osti.gov/biblio/1424875 Publication ID: 55131
  • Narayan, A., Jakeman, J., Zhou, T., & Zhou, T. (2017). A christoffel function weighted least squares algorithm for collocation approximations. Mathematics of Computation, 86(306), pp. 1913-1947. https://doi.org/10.1090/mcom/3192 Publication ID: 42643
  • Jakeman, J., Narayan, A., Zhou, T., & Zhou, T. (2017). A generalized sampling and preconditioning scheme for sparse approximation of polynomial chaos expansions. SIAM Journal on Scientific Computing, 39(3), pp. A1114-A1144. https://doi.org/10.1137/16m1063885 Publication ID: 48548
  • Tezaur, I., Salinger, A., Perego, M., Tuminaro, R., Jakeman, J., Eldred, M., Watkins, J., Price, S., Demeshko, I., & Demeshko, I. (2017). The Albany/FELIX Land-Ice Dynamical Core [Presentation]. https://www.osti.gov/biblio/1416697 Publication ID: 52719
  • Wildey, T., Jakeman, J., Butler, T., & Butler, T. (2016). A Consistent Bayesian Approach for Stochastic Inverse Problems [Conference Poster]. https://www.osti.gov/biblio/1368940 Publication ID: 50347
  • Jakeman, J., Narayan, A., Zhou, T., & Zhou, T. (2016). Efficient Sampling Schemes for Recovering Sparse PCE [Conference Poster]. https://www.osti.gov/biblio/1365093 Publication ID: 49363
  • Jakeman, J. (2016). Compressed sensing and its role in designing aircraft nozzles in the presence of uncertainty [Presentation]. https://www.osti.gov/biblio/1365225 Publication ID: 49538
  • Tezaur, I., Jakeman, J., Eldred, M., Perego, M., Salinger, A., Price, S., & Price, S. (2016). Towards Uncertainty Quantification in 21st Century Sea-Level Rise Predictions: Efficient Methods for Bayesian Calibration and Forward Propagation of Uncertainty for Land-Ice Models [Conference Poster]. https://www.osti.gov/biblio/1364846 Publication ID: 49071
  • Perego, M., Price, S., Stadler, G., Salinger, A., Tezaur, I., Eldred, M., Jakeman, J., & Jakeman, J. (2016). Towards Uncertainty Quantification in 21st Century SeaLevel Rise Predictions: PDE Constrained Optimization as a First Step in Bayesian Calibration and Forward Propagation [Conference Poster]. https://www.osti.gov/biblio/1366599 Publication ID: 49124
  • Perego, M., Jakeman, J., Price, S., Salinger, A., Stadler, G., Tezaur, I., & Tezaur, I. (2016). Computational Challenges in Ice Sheet Modeling [Conference Poster]. https://www.osti.gov/biblio/1366600 Publication ID: 49125
  • Rushdi, A., Swiler, L., Mitchell, S., Jakeman, J., Phipps, E., Ebeida, M., & Ebeida, M. (2016). VPS: Voronoi Piecewise Surrogate Models for High-Dimensional Data Fitting [Presentation]. https://doi.org/10.1615/Int.J.UncertaintyQuantification.2016018697 Publication ID: 46599
  • Perego, M., Eldred, M., Jakeman, J., Salinger, A., Tezaur, I., Price, S., Hoffman, M., & Hoffman, M. (2016). Towards quantifying uncertainty in Greenland’s contribution to 21st century sea-level rise [Conference Poster]. https://www.osti.gov/biblio/1339212 Publication ID: 46654
  • Asher, M., Jakeman, J., Jakeman, A., & Jakeman, A. (2015). Multifidelity surrogates of groundwater flow [Conference Poster]. https://www.osti.gov/biblio/1503957 Publication ID: 42060
  • Wildey, T., Shadid, J.N., Cyr, E., Jakeman, J., Butler, T., & Butler, T. (2015). Adjoint-Based a Posteriori Error Estimation and Uncertainty Quantification for Transient Nonlinear Problems with Discontinuous Solutions [Conference Poster]. https://www.osti.gov/biblio/1323036 Publication ID: 45375
  • Jakeman, J., Chen, Y., Xiu, D., Gittelson, C., & Gittelson, C. (2015). Dimension reduction for PDE using local Karhunen Loeve expansions. https://doi.org/10.2172/1221524 Publication ID: 45588
  • Wildey, T., Jakeman, J., & Jakeman, J. (2015). Adaptive Bayesian Inference for Prediction. https://doi.org/10.2172/1221574 Publication ID: 45595
  • Tezaur, I., Salinger, A., Perego, M., Jakeman, J., Eldred, M., Demeshko, I., Tuminaro, R., Price, S., & Price, S. (2015). Albany/FELIX: A Robust & Scalable Trilinos-Based Finite-Element Ice Flow Dycore Built for Advanced Architectures & Analysis [Conference Poster]. https://www.osti.gov/biblio/1301963 Publication ID: 44874
  • Jakeman, J. (2015). Multi-Variate Weighted Leja Sequences for Polynomial Approximation and UQ [Conference Poster]. https://www.osti.gov/biblio/1290921 Publication ID: 44943
  • Eldred, M., Debusschere, B., Chowdhary, K., Jakeman, J., Rai, P., Safta, C., Sargsyan, K., & Sargsyan, K. (2015). Sandia Software Enabling Extreme-Scale Uncertainty Quantification [Conference Poster]. https://www.osti.gov/biblio/1266821 Publication ID: 44419
  • Tezaur, I., Perego, M., Tuminaro, R., Salinger, A., Jakeman, J., Eldred, M., Ju, L., Zhang, T., Gunzburger, M., Price, S., & Price, S. (2015). Progress on the PISCEES FELIX Ice Sheet Dynamical Cores [Presentation]. https://www.osti.gov/biblio/1576124 Publication ID: 44422
  • Debusschere, B., Jakeman, J., Chowdhary, K., Safta, C., Sargsyan, K., Rai, P., Ghanem, R., Knio, O., La Maitre, O., Winokur, J., Li, G., Ghattas, O., Moser, R., Simmons, C., Alexanderian, A., Gattiker, J., Higdon, D., Lawrence, E., Bhat, S., … Parno, M. (2015). Quantification of Uncertainty in Extreme Scale Computations [Conference Poster]. https://www.osti.gov/biblio/1328212 Publication ID: 44684
  • Wildey, T., Jakeman, J., Butler, T., Cyr, E., Shadid, J.N., & Shadid, J.N. (2015). Adjoint-Based a Posteriori Error Estimation and Uncertainty Quantification for Shock-Hydrodynamic Applications [Conference Poster]. https://www.osti.gov/biblio/1279685 Publication ID: 44737
  • Wildey, T., Jakeman, J., Butler, T., & Butler, T. (2015). Utilizing Adjoint-based Error Estimates to Adaptively Resolve Response Surface Approximations [Conference Poster]. https://www.osti.gov/biblio/1256570 Publication ID: 43836
  • Jakeman, J. (2015). Sampling and Preconditioning Strategies for $\ell_1$-minimization [Conference Poster]. https://www.osti.gov/biblio/1253294 Publication ID: 43408
  • Eldred, M., Heimbach, P., Jackson, C., Jakeman, J., Perego, M., Price, S., Salinger, A., Stadler, G., Tezaur, I., & Tezaur, I. (2015). From Deterministic Inversion to Uncertainty Quantification: Planning a Long Journey in Ice Sheet Modeling [Conference Poster]. https://www.osti.gov/biblio/1246877 Publication ID: 42859
  • Perego, M., Price, S., Stadler, G., Eldred, M., Jackson, C., Jakeman, J., Salinger, A., Tezaur, I., & Tezaur, I. (2015). Advances in Ice Sheet Model Initialization Using the First Order Model [Conference Poster]. https://www.osti.gov/biblio/1245907 Publication ID: 42508
  • Chen, Y., Jakeman, J., Gittelson, C., Xiu, D., & Xiu, D. (2015). Local polynomial chaos expansion for linear differential equations with high dimensional random inputs. SIAM Journal on Scientific Computing, 37(1), pp. A79-A102. https://doi.org/10.1137/140970100 Publication ID: 42338
  • Eldred, M., Debusschere, B., Chowdhary, K., Jakeman, J., Najm, H.N., Safta, C., Sargsyan, K., & Sargsyan, K. (2014). Sandia Software Enabling Extreme-Scale Uncertainty Quantification [Presentation]. https://www.osti.gov/biblio/1494264 Publication ID: 37782
  • Najm, H.N., Eldred, M., Debusschere, B., Chowdhary, K., Jakeman, J., Safta, C., Sargsyan, K., & Sargsyan, K. (2014). An Overview of Select UQ Algorithms and their Utility in Applications [Presentation]. https://www.osti.gov/biblio/1494413 Publication ID: 37818
  • Adams, B., Jakeman, J., Swiler, L., Stephens, J., Vigil, D., Wildey, T., Bauman, L., Bohnhoff, W., Dalbey, K., Eddy, J., Ebeida, M., Eldred, M., Hough, P., Hu, K., & Hu, K. (2014). Dakota, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis version 6.0 theory manual. https://doi.org/10.2172/1177048 Publication ID: 40814
  • Adams, B., Jakeman, J., Swiler, L., Stephens, J., Vigil, D., Wildey, T., Bauman, L., Bohnhoff, W., Dalbey, K., Eddy, J., Ebeida, M., Eldred, M., Hough, P., Hu, K., & Hu, K. (2014). Dakota, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis :. https://doi.org/10.2172/1177077 Publication ID: 41017
  • Jakeman, J. (2014). A Posteriori Error Estimates to Enable Effective Dimension Reduction in Stochastic Systems [Conference]. https://www.osti.gov/biblio/1141488 Publication ID: 40247
  • Jakeman, J. (2014). Treating Computer Experiment: What Matters What Doesn’t What Evidence [Conference]. https://www.osti.gov/biblio/1141455 Publication ID: 40174
  • Jakeman, J. (2014). Practical identifiability analysis of environmental models [Conference]. https://www.osti.gov/biblio/1141678 Publication ID: 40193
  • Tezaur, I., Salinger, A., Perego, M., Tuminaro, R., Jakeman, J., & Jakeman, J. (2014). FELIX: The Albany Ice Sheet Modeling Code [Conference]. https://www.osti.gov/biblio/1140457 Publication ID: 36741
  • Jakeman, J. (2013). Polynomial Chaos Methods in Dakota [Conference]. https://www.osti.gov/biblio/1123391 Publication ID: 31950
  • Eldred, M., Jakeman, J., Wildey, T., & Wildey, T. (2013). Deployment of Scalable UQ Methods for High-Fidelity Simulation-based Applications within the DOE [Presentation]. https://www.osti.gov/biblio/1673675 Publication ID: 36511
  • Jakeman, J., Wildey, T., & Wildey, T. (2013). Scalable Uncertainty Quantification Methods [Presentation]. https://www.osti.gov/biblio/1666168 Publication ID: 34628
  • Jakeman, J., Eldred, M., & Eldred, M. (2013). Constructing Polynomial Chaos Expansions via Compressed Sensing and Cross Validation [Conference]. https://www.osti.gov/biblio/1106456 Publication ID: 34737
  • Jakeman, J. (2013). A Posteriori Error Analysis and Adaptive Construction of Surrogate Models [Conference]. https://www.osti.gov/biblio/1080030 Publication ID: 33723
  • Jakeman, J. (2013). High-dimensional sparse grid interpolation and quadrature using one-dimensional Leja quadrature rules [Conference]. https://www.osti.gov/biblio/1073318 Publication ID: 32913
  • Salinger, A., Tezaur, I., Perego, M., Tuminaro, R., Eldred, M., Jakeman, J., & Jakeman, J. (2013). Rapid Development of an Ice Sheet Climate Application using the Components-Based Approach [Presentation]. https://www.osti.gov/biblio/1661056 Publication ID: 33356
  • Eldred, M., Domino, S., Barone, M., Jakeman, J., & Jakeman, J. (2013). Advances in UQ Algorithms for Wind Energy Applications [Conference]. https://www.osti.gov/biblio/1062946 Publication ID: 31642
  • Jakeman, J., Wildey, T., & Wildey, T. (2013). Quantifying Uncertainty using a-posteriori Enhanced Sparse Grid Approximations [Conference]. https://www.osti.gov/biblio/1063316 Publication ID: 32169
  • Jakeman, J. (2013). Constructing Polynomial Chaos Expansions via Compressed Sensing and Cross Validation [Conference]. https://www.osti.gov/biblio/1063474 Publication ID: 31245
  • Najm, H.N., Sargsyan, K., Safta, C., Debusschere, B., Jakeman, J., Eldred, M., & Eldred, M. (2012). Sparse Polynomial Representations of High Dimensional Models [Conference]. https://www.osti.gov/biblio/1073443 Publication ID: 28932
  • Jakeman, J., Wildey, T., Eldred, M., & Eldred, M. (2012). Adaptive sparse grids for uncertainty quantication Enhancing approximations using a posteriori error estimation [Conference]. https://www.osti.gov/biblio/1073416 Publication ID: 29105
  • Jakeman, J. (2012). A Discussion of Gaussian Process Models and Polynomial Chaos Methods for Uncertainty Quantification [Conference]. https://www.osti.gov/biblio/1073376 Publication ID: 28229
  • Jakeman, J. (2012). Locally Adaptive Generalised Sparse Grids [Conference]. https://www.osti.gov/biblio/1078654 Publication ID: 27513
  • Najm, H.N., Debusschere, B., Eldred, M., Safta, C., Jakeman, J., & Jakeman, J. (2012). Quantification of Uncertainty in Extreme Scale Computations (QUEST) [Conference]. https://www.osti.gov/biblio/1078675 Publication ID: 27011
  • Jakeman, J. (2012). Minimal Multi-Element Stochastic Collocation for Uncertainty Quantification of Discontinuous Functions. Journal of Computational Physics. https://www.osti.gov/biblio/1078731 Publication ID: 26724
Showing 10 of 147 publications.

Projects

FASTMath
CHaRMNET: Center for Hierarchical and Robust Modeling of Non-Equilibrium Transport

Software

PyApprox