John Davis Jakeman

John Jakeman, Michael Eldred, Gianluca Geraci, Daniel Seidl, Thomas Smith, Alex Gorodetsky, Trung Pham, Akil Narayan, Xiaoshu Zeng, Roger Ghanem, (2022). Multi-fidelity information fusion and resource allocation https://doi.org/10.2172/1888363 Publication ID: 80245

Irina Tezaur, Kara Peterson, Amy Powell, John Jakeman, Erika Roesler, (2022). Global Sensitivity Analysis Using the Ultra‐Low Resolution Energy Exascale Earth System Model Journal of Advances in Modeling Earth Systems https://doi.org/10.1029/2021MS002831 Publication ID: 80057

John Jakeman, (2022). PyApprox: Enabling efficient model analysis https://doi.org/10.2172/1879614 Publication ID: 80040

John Jakeman, Sam Friedman, Michael Eldred, Lorenzo Tamellini, Alex Gorodetsky, Doug Allaire, (2022). Adaptive experimental design for multi-fidelity surrogate modeling of multi-disciplinary systems International Journal for Numerical Methods in Engineering https://doi.org/10.1002/nme.6958 Publication ID: 80511

John Jakeman, Drew Kouri, Jose Huerta, (2022). Surrogate modeling for efficiently, accurately and conservatively estimating measures of risk Reliability Engineering and System Safety https://doi.org/10.1016/j.ress.2021.108280 Publication ID: 80231

Qian Wang, Joseph Guillaume, John Jakeman, Tao Yang, Takuya Iwanaga, Barry Croke, Anthony Jakeman, (2022). Assessing the predictive impact of factor fixing with an adaptive uncertainty-based approach Environmental Modelling and Software https://doi.org/10.1016/j.envsoft.2021.105290 Publication ID: 80234

Alex Gorodetsky, Cosmin Safta, John Jakeman, (2022). Reverse-mode differentiation in arbitrary tensor network format: with application to supervised learning Journal of Machine Learning Research https://www.osti.gov/servlets/purl/1872019 Publication ID: 80727

Michael Eldred, Gianluca Geraci, Alex Gorodetsky, John Jakeman, Teresa Portone, Timothy Wildey, Ahmad Rushdi, Daniel Seidl, (2021). The Dakota Project: Connecting the Pipeline from Uncertainty Quantification R&D to Mission Impact https://www.osti.gov/servlets/purl/1891078 Publication ID: 76127

R. White, John Jakeman, Bart van Bloemen Waanders, Drew Kouri, Alex Alexanderian, (2021). Exploring risk-averse design criteria for sequential optimal experimental design in a Bayesian setting https://doi.org/10.2172/1888463 Publication ID: 75823

Zachary Morrow, Bart van Bloemen Waanders, John Jakeman, (2021). Characterizing Approximation Methods for Digital Twins in Scientific Computing https://doi.org/10.2172/1889008 Publication ID: 75870

Daniel Seidl, John Jakeman, (2021). Improving Digital Twins by Learning from a Fleet of Assets https://doi.org/10.2172/1889023 Publication ID: 75878

John Jakeman, Drew Kouri, Jose Huerta, (2021). Surrogate Modeling For Efficiently, Accurately and Conservatively Estimating Measures of Risk https://doi.org/10.2172/1889571 Publication ID: 75892

Drew Kouri, John Jakeman, Jose Huerta, Timothy Walsh, Chandler Smith, Stan Uryasev, (2021). Risk-Adaptive Experimental Design for High-Consequence Systems: LDRD Final Report https://doi.org/10.2172/1820307 Publication ID: 75666

Sam Friedman, John Jakeman, Michael Eldred, Lorenzo Tamellini, Alex Gorodestky, Doug Allaire, (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

John Jakeman, Drew Kouri, Jose Huerta, (2021). Surrogate Modeling For Efficiently Accurately and Conservatively Estimating Measures of Risk https://doi.org/10.2172/1807455 Publication ID: 78808

John Jakeman, Michael Eldred, Gianluca Geraci, Teresa Portone, Ahmad Rushdi, Daniel Seidl, Thomas Smith, (2021). Multi-fidelity Machine Learning https://doi.org/10.2172/1876608 Publication ID: 79162

Xiaoshu Zeng, Gianluca Geraci, Michael Eldred, John Jakeman, Alex Gorodetsky, Roger Ghanem, (2021). Adaptive Basis for Multifidelity Uncertainty Quantification https://doi.org/10.2172/1889016 Publication ID: 79504

Michael Eldred, Gianluca Geraci, Alex Gorodetsky, John Jakeman, Teresa Portone, (2021). Efficient Deployment of Multifidelity Sampling Methods in Production Settings https://doi.org/10.2172/1882491 Publication ID: 79508

Timothy Wildey, Troy Butler, John Jakeman, Anh Tran, (2021). Solving Stochastic Inverse Problems for Property-Structure Relationships in Computational Materials Science https://doi.org/10.2172/1890916 Publication ID: 79535

John Jakeman, Samuel Friedman, Michael Eldred, Lorenzo Tamellini, Alex Gorodetsky, Doug Allaire, (2021). Adaptive resource allocation for surrogate modeling of systems comprised of multiple disciplines with varying fidelity https://doi.org/10.2172/1872879 Publication ID: 78820

William Reese, Joseph Hart, Bart van Bloemen Waanders, Mauro Perergo, John Jakeman, Arvind Saibaba, (2021). Bedrock Inversion and Hyper Differential Sensitivity Analysis for the Shallow Ice Model https://www.osti.gov/servlets/purl/1889590 Publication ID: 78605

Tong Qin, Zhen Chen, John Jakeman, Dongbin Xiu, (2021). Data-driven learning of nonautonomous systems SIAM Journal on Scientific Computing https://doi.org/10.1137/20m1342859 Publication ID: 75804

Timothy Wildey, Troy Butler, John Jakeman, (2021). Combining Measure Theory and Bayes? Rule to Solve a Stochastic Inverse Problem https://doi.org/10.2172/1877851 Publication ID: 78143

Helumt Harbrecht, John Jakeman, Peter Zaspel, (2021). Cholesky-based experimental design for gaussian process and kernel-based emulation and calibration Communications in Computational Physics https://doi.org/10.4208/cicp.OA-2020-0060 Publication ID: 71546

Cosmin Safta, Khachik Sargsyan, John Jakeman, Alex Gorodetsky, (2021). Low-Rank Tensor Network Approximations for Earth System Model https://doi.org/10.2172/1854317 Publication ID: 77458

John Jakeman, Alex Gorodetsky, Michael Eldred, Gianluca Geraci, Thomas Smith, (2021). MFNETS: Multi-Fidelity Data-Driven Networks for Data Analysis https://doi.org/10.2172/1854429 Publication ID: 77472

Saman Razavi, Anthony Jakeman, Andrea Saltelli, Clémentine Prieur, Bertrand Iooss, Emanuele Borgonovo, Elmar Plischke, Samuele Lo Piano, Takuya Iwanaga, William Becker, Stefano Tarantola, Joseph Guillaume, John Jakeman, Hoshin Gupta, Nicola Melillo, Giovanni Rabitti, Vincent Chabridon, Qingyun Duan, Xifu Sun, Stefán Smith, Razi Sheikholeslami, Nasim Hosseini, Masoud Asadzadeh, Arnald Puy, Sergei Kucherenko, Holger Maier, (2021). The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support Environmental Modelling and Software https://doi.org/10.1016/j.envsoft.2020.104954 Publication ID: 74992

Michael Eldred, Alex Gorodetsky, Gianluca Geraci, John Jakeman, Teresa Portone, (2021). Recent Advances in Adaptive Refinement of (Regression-Based) Multifidelity Surrogates for UQ https://doi.org/10.2172/1847573 Publication ID: 77372

Bert Debusschere, Gianluca Geraci, John Jakeman, Cosmin Safta, Laura Swiler, (2021). Polynomial Chaos Expansions for Discrete Random Variables in Cyber Security Emulytics Experiments https://doi.org/10.2172/1847628 Publication ID: 77383

Laura Swiler, Mamikon Gulian, A. Frankel, Cosmin Safta, John Jakeman, (2021). Constrained Gaussian Processes: A Survey https://doi.org/10.2172/1847480 Publication ID: 77280

Tong Qin, Zhen Chen, John Jakeman, Dongbin Xiu, (2021). Deep learning of parameterized equations with applications to uncertainty quantification International Journal for Uncertainty Quantification https://doi.org/10.1615/int.j.uncertaintyquantification.2020034123 Publication ID: 71096

Alex Gorodetsky, Kazuya Tsuji, John Jakeman, Gianluca Geraci, Michael Eldred, (2020). Multifidelity information fusion via network models for uncertainty quantification in aerospace dynamical systems https://doi.org/10.2172/1836910 Publication ID: 72268

Bill Lozanovski, David Downing, Rance Tino, Anton du Plessis, Phuong Tran, John Jakeman, Darpan Shidid, Claus Emmelmann, Ma Qian, Peter Choong, Milan Brandt, Martin Leary, (2020). Non-destructive simulation of node defects in additively manufactured lattice structures Additive Manufacturing https://doi.org/10.1016/j.addma.2020.101593 Publication ID: 74924

Gianluca Geraci, Michael Eldred, Alex Gorodetsky, John Jakeman, (2020). Multifidelity Strategies in UQ: an overview on some recent trends in sampling based approaches https://www.osti.gov/servlets/purl/1822111 Publication ID: 74972

Mamikon Gulian, Laura Swiler, A. Frankel, Cosmin Safta, John Jakeman, (2020). A Survey of Constrained Gaussian Process Regression: Approaches and Implementation Challenges https://www.osti.gov/servlets/purl/1814448 Publication ID: 74592

Mamikon Gulian, Laura Swiler, A. Frankel, John Jakeman, Cosmin Safta, (2020). A Survey of Constrained Gaussian Process Regression: Approaches and Implementation Challenges https://www.osti.gov/servlets/purl/1812282 Publication ID: 74359

Alex Gorodetsky, Gianluca Geraci, Michael Eldred, John Jakeman, (2020). A generalized approximate control variate framework for multifidelity uncertainty quantification Journal of Computational Physics https://doi.org/10.1016/j.jcp.2020.109257 Publication ID: 71027

John Jakeman, Michael Eldred, Gianluca Geraci, Alex Gorodetsky, (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

Alex Gorodetsky, John Jakeman, Gianluca Geraci, Michael Eldred, (2020). Mfnets: Multi-fidelity data-driven networks for bayesian learning and prediction International Journal for Uncertainty Quantification https://doi.org/10.1615/Int.J.UncertaintyQuantification.2020032978 Publication ID: 74093

Cosmin Safta, Khachik Sargsyan, John Jakeman, (2019). Uncertainty Quantification for E3SM Land Component using Low-Rank Surrogate Models https://www.osti.gov/servlets/purl/1643449 Publication ID: 66773

Timothy Wildey, Lukas Bruder, Tan Bui-Thanh, Troy Butler, John Jakeman, Brad Marvin, Anh Tran, Scott Walsh, (2019). Moving Beyond Forward Simulation to Enable Data-informed Physics-based Predictions https://www.osti.gov/biblio/1646273 Publication ID: 66318

John Jakeman, (2019). Uncertainty Quantification: An Overview https://www.osti.gov/servlets/purl/1643293 Publication ID: 66341

Gianluca Geraci, Michael Eldred, Alex Gorodetsky, John Jakeman, (2019). Recent advancement in Multifidelity Uncertainty Quantification https://www.osti.gov/servlets/purl/1642820 Publication ID: 65621

Timothy Wildey, Troy Butler, John Jakeman, (2019). Convergence of Probability Densities using Approximate Models for Forward and Inverse Problems in Uncertainty Quantification https://www.osti.gov/servlets/purl/1641989 Publication ID: 64879

Michael Eldred, Gianluca Geraci, Alex Gorodetsky, John Jakeman, (2019). Multilevel / Multifidelity Sampling and Emulation for Forward UQ https://www.osti.gov/servlets/purl/1645988 Publication ID: 65019

Gianluca Geraci, Michael Eldred, Alex Gorodetsky, John Jakeman, (2019). Recent Advancements for Multifidelity UQ and OUU in Dakota: Capability Overview and Perspectives https://www.osti.gov/servlets/purl/1641419 Publication ID: 70164

John Jakeman, Fabian Franzelin, Akil Narayan, Michael Eldred, Dirk Plfüger, (2019). Polynomial chaos expansions for dependent random variables Computer Methods in Applied Mechanics and Engineering https://doi.org/10.1016/j.cma.2019.03.049 Publication ID: 63130

Cosmin Safta, Timothy Reid, John Jakeman, Khachik Sargsyan, (2019). Approximating Data with Stochastic and Physical Dependence using the Functional Tensor Train Models https://www.osti.gov/servlets/purl/1641238 Publication ID: 69801

Timothy Wildey, Troy Butler, John Jakeman, Lukas Bruder, (2019). Solving Stochastic Inverse Problems using Approximate Push-forward Densities based on a Multi-fidelity Monte Carlo Method https://www.osti.gov/servlets/purl/1641047 Publication ID: 69562

Michael Eldred, Gianluca Geraci, Alex Gorodetsky, John Jakeman, (2019). Experience with Multilevel/Multifidelity/Multi-Index Sampling and Surrogate Approaches for Forward Uncertainty Quantification https://www.osti.gov/servlets/purl/1641388 Publication ID: 70119

Tiernan Casey, Bert Debusschere, Michael Eldred, Gianluca Geraci, Roger Ghanem, John Jakeman, Youssef Marzouk, Habib Najm, Cosmin Safta, Khachik Sargsyan, (2019). FASTMath: UQ Algorithms https://www.osti.gov/servlets/purl/1641088 Publication ID: 69621

Cosmin Safta, John Jakeman, Alex Gorodetsky, (2019). Low-Rank Functional Tensor Train Representations for High-Dimensional Computational Models https://www.osti.gov/servlets/purl/1645344 Publication ID: 69010

Cosmin Safta, Khachik Sargsyan, John Jakeman, Alex Gorodetsky, Daniel Ricciuto, (2019). Exploiting Model Structure for Forward Propagation of Uncertainty in Earth System Models https://www.osti.gov/servlets/purl/1640926 Publication ID: 69353

John Jakeman, (2019). A mathematical perspective on the certification and design of physical systems in the presence of uncertainty https://www.osti.gov/biblio/1645241 Publication ID: 68525

Luca Bertagna, John Jakeman, Mauro Perego, Irina Tezaur, Jerry Watkins, Andrew Salinger, Xylar Asay-Davis, Matthew Hoffman, Stephen Price, Tong Zhang, Georg Stadler, (2019). Modeling Ice Sheets with MALI https://www.osti.gov/servlets/purl/1645332 Publication ID: 68862

Gianluca Geraci, Alex Gorodetsky, Michael Eldred, John Jakeman, (2019). Recent advancements toward generalized sampling strategies for multifidelity Uncertainty Quantification https://www.osti.gov/servlets/purl/1644568 Publication ID: 67615

Cosmin Safta, Khachik Sargsyan, John Jakeman, Alex Gorodetsky, Daniel Ricciuto, (2019). Exploiting Low-Rank Structure for Sensitivity Analysis in Earth System Models https://www.osti.gov/servlets/purl/1639271 Publication ID: 67283

Mauro Perego, John Jakeman, William Severa, Lars Ruthotto, (2019). Neural Networks Surrogates of PDE-based Dynamical Systems: Application to Ice Sheet Dynamics https://www.osti.gov/servlets/purl/1639251 Publication ID: 67249

Timothy Wildey, Troy Butler, John Jakeman, Tan Bui-Thanh, Brad Marvin, Lukas Bruder, (2019). Developing Scalable and Multi-fidelity Approaches for Push-forward Based Inference https://www.osti.gov/servlets/purl/1596420 Publication ID: 64514

Gianluca Geraci, Michael Eldred, Alex Gorodetsky, John Jakeman, (2019). Recent advancements in Multilevel-Multifidelity techniques for forward UQ in the DARPA Sequoia project https://doi.org/10.2514/6.2019-0722 Publication ID: 64164

Cosmin Safta, Dan Ricciuto, Alex Gorodetsky, John Jakeman, (2018). Exploiting Model Structure for Global Sensitivity Analysis in E3SM Land Model https://www.osti.gov/servlets/purl/1761160 Publication ID: 60522

Alex Gorodetsky, John Jakeman, (2018). Gradient-based optimization for regression in the functional tensor-train format Journal of Computational Physics https://doi.org/10.1016/j.jcp.2018.08.010 Publication ID: 60350

Timothy Wildey, Troy Butler, John Jakeman, (2018). The Consistent Bayesian Approach for Stochastic Inverse Problems https://www.osti.gov/servlets/purl/1592669 Publication ID: 59876

John Jakeman, Mauro Perego, William Severa, (2018). Neural Networks as Surrogates of Nonlinear High-Dimensional Parameter-to-Prediction Maps https://doi.org/10.2172/1531317 Publication ID: 59302

John Jakeman, Akil Narayan, (2018). Generation and application of multivariate polynomial quadrature rules Computer Methods in Applied Mechanics and Engineering https://doi.org/10.1016/j.cma.2018.04.009 Publication ID: 60352

Michael Eldred, Gianluca Geraci, Alex Gorodetsky, John Jakeman, (2018). Lecture 1: Multilevel-Multifidelity with Monte Carlo Sampling; Algorithms and deployment experience https://www.osti.gov/servlets/purl/1582192 Publication ID: 63755

John Jakeman, Troy Butler, Michael Eldred, Gianluca Geraci, Alex Gorodetsky, Timothy Wildey, (2018). Adaptive multi-index collocation for quantifying uncertainty https://www.osti.gov/servlets/purl/1806541 Publication ID: 63211

Timothy Wildey, Troy Butler, John Jakeman, Brad Marvin, (2018). Consistent Bayesian Inference with Push-forward Measures: Scalable Implementations and Applications https://www.osti.gov/servlets/purl/1567819 Publication ID: 62972

John Jakeman, Mauro Perego, Irina Tezaur, Stephen Price, Georg Stadler, (2018). Ice Sheet Initialization and Uncertainty Quantification of SeaLevel Rise https://www.osti.gov/servlets/purl/1523714 Publication ID: 62288

Timothy Wildey, Troy Butler, John Jakeman, Daniel Seidl, Bart van Bloemen Waanders, (2018). Data-informed Multiscale Modeling of Additive Materials https://www.osti.gov/servlets/purl/1523778 Publication ID: 62297

Mauro Perego, Luca Bertagna, Matthew Hoffman, John Jakeman, Stephen Price, Andrew Salinger, Georg Stadler, Irina Tezaur, Jerry Watkins, (2018). Ice Sheet Modeling: Computational and Mathematical Challenges https://www.osti.gov/servlets/purl/1513472 Publication ID: 62055

Mauro Perego, John Jakeman, Mauro Perego, Irina Tezaur, Stephen Price, Georg Stadler, (2018). Methodologies for Enabling Bayesian Calibration in Landice Modeling Towards Probabilistic Projections of Sealevel Change https://www.osti.gov/servlets/purl/1510847 Publication ID: 61720

Gianluca Geraci, Alex Gorodetsky, Michael Eldred, John Jakeman, (2018). TOWARDS LEVERAGING ACTIVE DIRECTION FOR EFFICIENT MULTIFIDELITY UQ STRATEGIES https://www.osti.gov/servlets/purl/1525631 Publication ID: 61779

Timothy Wildey, Troy Butler, John Jakeman, Scott Walsh, (2018). Optimal Experimental Design for Prediction Using a Consistent Bayesian Approach https://www.osti.gov/servlets/purl/1507835 Publication ID: 61612

Ben Adcock, Anyi Bao, John Jakeman, Akil Naryan, (2018). Compressed sensing with sparse corruptions: Fault-tolerant sparse collocation approximations https://doi.org/10.2172/1434573 Publication ID: 61625

Michael Eldred, Gianluca Geraci, Alex Gorodetsky, John Jakeman, (2018). Adaptive Refinement Strategies for Multilevel Polynomial Chaos Expansions https://www.osti.gov/servlets/purl/1575179 Publication ID: 61666

Anthony Jakeman, John Jakeman, (2018). An overview of methods to identify and manage uncertainty for modelling problems in the water-environment-agriculture cross-sector Mathematics for Industry https://doi.org/10.1007/978-981-10-7811-8_15 Publication ID: 58446

Scott Walsh, Timothy Wildey, John Jakeman, (2018). Optimal experimental design using a consistent Bayesian approach ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering https://doi.org/10.1115/1.4037457 Publication ID: 56168

Cosmin Safta, John Jakeman, Roger Ghanem, (2018). Scalable Uncertainty Quantification: Exploiting Structure in Models and Data https://www.osti.gov/servlets/purl/1497534 Publication ID: 60773

Michael Eldred, Gianluca Geraci, Alex Gorodetsky, John Jakeman, (2018). Multilevel-Multifidelity Approaches for Forward UQ in the DARPA SEQUOIA Project https://www.osti.gov/servlets/purl/1513488 Publication ID: 58681

Alex Gorodetsky, Gianluca Geraci, Michael Eldred, John Jakeman, (2018). Multifidelity Model Management using Latent Variable Bayesian Networks https://www.osti.gov/servlets/purl/1513639 Publication ID: 58728

Kara Peterson, Michael Parks, Eric Ackerman, Ray Bambha, Diana Bull, Jennifer Frederick, Jasper Hardesty, Anastasia Ilgen, John Jakeman, Amy Powell, Matthew Peterson, Erika Roesler, Cosmin Safta, David Stracuzzi, Irina Tezaur, (2018). Arctic Tipping Points Triggering Global Change https://www.osti.gov/servlets/purl/1513640 Publication ID: 58729

John Jakeman, Roland Pulch, (2018). Time and Frequency Domain Methods for Basis Selection in Random Linear Dynamical Systems International Journal for Uncertainty Quantification https://doi.org/10.1615/Int.J.UncertaintyQuantification.2018026902 Publication ID: 59010

Ben Adcock, Anyi Bao, John Jakeman, Akil Narayan, (2018). Compressed sensing with sparse corruptions: Fault-tolerant sparse collocation approximations SIAM-ASA Journal on Uncertainty Quantification https://doi.org/10.1137/17M112590X Publication ID: 59018

John Jakeman, Mauro Perego, Irina Tezaur, Steve Price, (2017). Towards probabilistic predictions of future sea-level https://www.osti.gov/servlets/purl/1481488 Publication ID: 54018

John Jakeman, Akil Narayan, (2017). Generation and application of multivariate polynomial quadrature rules https://doi.org/10.2172/1510651 Publication ID: 54017

Timothy Wildey, Troy Butler, John Jakeman, (2017). A Consistent Bayesian Approach for Solving Stochastic Inverse Problems https://www.osti.gov/servlets/purl/1469097 Publication ID: 58335

Timothy Wildey, John Jakeman, Troy Butler, (2017). Advancing Beyond Interpretive Simulation to Inference for Prediction https://www.osti.gov/servlets/purl/1467988 Publication ID: 58203

John Jakeman, Alex Gorodetsky, Michael Eldred, (2017). Tractable Uncertainty Quantification: Exploiting Structure https://www.osti.gov/servlets/purl/1466103 Publication ID: 58076

Gianluca Geraci, Alex Gorodetsky, John Jakeman, Michael Eldred, (2017). Sampling Polynomial Chaos and Functional Tensor Train Multilevel/Multifidelity Strategies for Forward UQ https://www.osti.gov/servlets/purl/1507076 Publication ID: 57348

Michael Eldred, Gianluca Geraci, Alex Gorodetsky, John Jakeman, (2017). Multilevel-Multifidelity Expansions with Application to Forward UQ OUU and Emulator-Based Bayesian Inference https://www.osti.gov/servlets/purl/1507501 Publication ID: 57417

Irina Tezaur, John Jakeman, Michael Eldred, Mauro Perego, Stephen Price, Andrew Salinger, (2017). Large-scale Deterministic Inversion and Bayesian Calibration in Land-Ice Modeling https://www.osti.gov/servlets/purl/1460158 Publication ID: 57170

Michael Eldred, Jason Monschke, John Jakeman, Gianluca Geraci, (2017). Multilevel-Multifidelity Approaches for Uncertainty Quantification and Design https://www.osti.gov/servlets/purl/1455372 Publication ID: 56819

John Jakeman, (2017). Multivariate Quadrature Rules for Correlated Random Variables https://www.osti.gov/servlets/purl/1427962 Publication ID: 55379

Alex Gorodetsky, John Jakeman, (2017). High-dimensional regression of low-rank functions https://www.osti.gov/servlets/purl/1426383 Publication ID: 55240

Timothy Wildey, John Jakeman, Troy Butler, (2017). Efficient Sampling Strategies for the Consistent Bayesian Approach for Solving Stochastic Inverse Problems https://www.osti.gov/servlets/purl/1425298 Publication ID: 55046

Anyi Bao, Ben Adcock, John Jakeman, Akil Narayan, (2017). Compressive Sampling in Multivariate Polynomial Approximation with Corrupted Simulation Samples https://www.osti.gov/servlets/purl/1424875 Publication ID: 55131

Akil Narayan, John Jakeman, Tao Zhou, (2017). A christoffel function weighted least squares algorithm for collocation approximations Mathematics of Computation https://doi.org/10.1090/mcom/3192 Publication ID: 42643

John Jakeman, Akil Narayan, Tao Zhou, (2017). A generalized sampling and preconditioning scheme for sparse approximation of polynomial chaos expansions SIAM Journal on Scientific Computing https://doi.org/10.1137/16m1063885 Publication ID: 48548

Irina Tezaur, Andrew Salinger, Mauro Perego, Raymond Tuminaro, John Jakeman, Michael Eldred, Jerry Watkins, Stephen Price, Irina Demeshko, (2017). The Albany/FELIX Land-Ice Dynamical Core https://www.osti.gov/servlets/purl/1416697 Publication ID: 52719

Timothy Wildey, John Jakeman, Troy Butler, (2016). A Consistent Bayesian Approach for Stochastic Inverse Problems https://www.osti.gov/servlets/purl/1368940 Publication ID: 50347

John Jakeman, (2016). Compressed sensing and its role in designing aircraft nozzles in the presence of uncertainty https://www.osti.gov/servlets/purl/1365225 Publication ID: 49538

John Jakeman, Akil Narayan, Tao Zhou, (2016). Efficient Sampling Schemes for Recovering Sparse PCE https://www.osti.gov/servlets/purl/1365093 Publication ID: 49363

Irina Tezaur, John Jakeman, Michael Eldred, Mauro Perego, Andrew Salinger, Stephen Price, (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 https://www.osti.gov/servlets/purl/1364846 Publication ID: 49071

Mauro Perego, S. Price, G. Stadler, Andrew Salinger, Irina Tezaur, Michael Eldred, John Jakeman, (2016). Towards Uncertainty Quantification in 21st Century SeaLevel Rise Predictions: PDE Constrained Optimization as a First Step in Bayesian Calibration and Forward Propagation https://www.osti.gov/servlets/purl/1366599 Publication ID: 49124

Mauro Perego, John Jakeman, S. Price, Andrew Salinger, G. Stadler, Irina Tezaur, (2016). Computational Challenges in Ice Sheet Modeling https://www.osti.gov/servlets/purl/1366600 Publication ID: 49125

Ahmad Rushdi, Laura Swiler, Scott Mitchell, John Jakeman, Eric Phipps, Mohamed Ebeida, (2016). VPS: Voronoi Piecewise Surrogate Models for High-Dimensional Data Fitting https://doi.org/10.1615/Int.J.UncertaintyQuantification.2016018697 Publication ID: 46599

Mauro Perego, Michael Eldred, John Jakeman, Andrew Salinger, Irina Tezaur, Stephen Price, Matthew Hoffman, (2016). Towards quantifying uncertainty in Greenland’s contribution to 21st century sea-level rise https://www.osti.gov/servlets/purl/1339212 Publication ID: 46654

Michael Asher, John Jakeman, Anthony Jakeman, (2015). Multifidelity surrogates of groundwater flow https://www.osti.gov/servlets/purl/1503957 Publication ID: 42060

John Jakeman, Yi Chen, Dongbin Xiu, Claude Gittelson, (2015). Dimension reduction for PDE using local Karhunen Loeve expansions https://doi.org/10.2172/1221524 Publication ID: 45588

Timothy Wildey, John Jakeman, (2015). Adaptive Bayesian Inference for Prediction https://doi.org/10.2172/1221574 Publication ID: 45595

Timothy Wildey, John Shadid, Eric Cyr, John Jakeman, Troy Butler, (2015). Adjoint-Based a Posteriori Error Estimation and Uncertainty Quantification for Transient Nonlinear Problems with Discontinuous Solutions https://www.osti.gov/servlets/purl/1323036 Publication ID: 45375

Irina Tezaur, Andrew Salinger, Mauro Perego, John Jakeman, Michael Eldred, Irina Demeshko, Raymond Tuminaro, Stephen Price, (2015). Albany/FELIX: A Robust & Scalable Trilinos-Based Finite-Element Ice Flow Dycore Built for Advanced Architectures & Analysis https://www.osti.gov/servlets/purl/1301963 Publication ID: 44874

John Jakeman, (2015). Multi-Variate Weighted Leja Sequences for Polynomial Approximation and UQ https://www.osti.gov/servlets/purl/1290921 Publication ID: 44943

Michael Eldred, Bert Debusschere, Kamaljit Chowdhary, John Jakeman, Prashant Rai, Cosmin Safta, Khachik Sargsyan, (2015). Sandia Software Enabling Extreme-Scale Uncertainty Quantification https://www.osti.gov/servlets/purl/1266821 Publication ID: 44419

Irina Tezaur, Mauro Perego, Raymond Tuminaro, Andrew Salinger, John Jakeman, Michael Eldred, Lili Ju, Tong Zhang, Max Gunzburger, Stephen Price, (2015). Progress on the PISCEES FELIX Ice Sheet Dynamical Cores https://www.osti.gov/servlets/purl/1576124 Publication ID: 44422

Bert Debusschere, John Jakeman, Kamaljit Chowdhary, Cosmin Safta, Khachik Sargsyan, P. Rai, R. Ghanem, O. Knio, O. La Maitre, J. Winokur, G. Li, O. Ghattas, R. Moser, C. Simmons, A. Alexanderian, J. Gattiker, D. Higdon, E. Lawrence, S. Bhat, Y. Marzouk, D. Bigoni, T. Cui, M. Parno, (2015). Quantification of Uncertainty in Extreme Scale Computations https://www.osti.gov/servlets/purl/1328212 Publication ID: 44684

Timothy Wildey, John Jakeman, Troy Butler, Eric Cyr, John Shadid, (2015). Adjoint-Based a Posteriori Error Estimation and Uncertainty Quantification for Shock-Hydrodynamic Applications https://www.osti.gov/servlets/purl/1279685 Publication ID: 44737

Timothy Wildey, John Jakeman, Troy Butler, (2015). Utilizing Adjoint-based Error Estimates to Adaptively Resolve Response Surface Approximations https://www.osti.gov/servlets/purl/1256570 Publication ID: 43836

John Jakeman, (2015). Sampling and Preconditioning Strategies for $\ell_1$-minimization https://www.osti.gov/servlets/purl/1253294 Publication ID: 43408

Michael Eldred, Patrick Heimbach, Charles Jackson, John Jakeman, Mauro Perego, Stephen, Price, Andrew Salinger, Georg Stadler, Irina Tezaur, (2015). From Deterministic Inversion to Uncertainty Quantification: Planning a Long Journey in Ice Sheet Modeling https://www.osti.gov/servlets/purl/1246877 Publication ID: 42859

Mauro Perego, Stephen Price, Georg Stadler, Michael Eldred, Charles Jackson, John Jakeman, Andrew Salinger, Irina Tezaur, (2015). Advances in Ice Sheet Model Initialization Using the First Order Model https://www.osti.gov/servlets/purl/1245907 Publication ID: 42508

Yi Chen, John Jakeman, Claude Gittelson, Dongbin Xiu, (2015). Local polynomial chaos expansion for linear differential equations with high dimensional random inputs SIAM Journal on Scientific Computing https://doi.org/10.1137/140970100 Publication ID: 42338

Michael Eldred, Bert Debusschere, Kamaljit Chowdhary, John Jakeman, Habib Najm, Cosmin Safta, Khachik Sargsyan, (2014). Sandia Software Enabling Extreme-Scale Uncertainty Quantification https://www.osti.gov/servlets/purl/1494264 Publication ID: 37782

Habib Najm, Michael Eldred, Bert Debusschere, Kamaljit Chowdhary, John Jakeman, Cosmin Safta, Khachik Sargsyan, (2014). An Overview of Select UQ Algorithms and their Utility in Applications https://www.osti.gov/servlets/purl/1494413 Publication ID: 37818

Brian Adams, John Jakeman, Laura Swiler, John Stephens, Dena Vigil, Timothy Wildey, Lara Bauman, William Bohnhoff, Keith Dalbey, John Eddy, Mohamed Ebeida, Michael Eldred, Patricia Hough, Kenneth Hu, (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

Brian Adams, John Jakeman, Laura Swiler, John Stephens, Dena Vigil, Timothy Wildey, Lara Bauman, William Bohnhoff, Keith Dalbey, John Eddy, Mohamed Ebeida, Michael Eldred, Patricia Hough, Kenneth Hu, (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

John Jakeman, (2014). A Posteriori Error Estimates to Enable Effective Dimension Reduction in Stochastic Systems https://www.osti.gov/servlets/purl/1141488 Publication ID: 40247

John Jakeman, (2014). Practical identifiability analysis of environmental models https://www.osti.gov/servlets/purl/1141678 Publication ID: 40193

John Jakeman, (2014). Treating Computer Experiment: What Matters What Doesn’t What Evidence https://www.osti.gov/servlets/purl/1141455 Publication ID: 40174

Irina Tezaur, Andrew Salinger, Mauro Perego, Raymond Tuminaro, John Jakeman, (2014). FELIX: The Albany Ice Sheet Modeling Code https://www.osti.gov/servlets/purl/1140457 Publication ID: 36741

John Jakeman, (2013). Polynomial Chaos Methods in Dakota https://www.osti.gov/servlets/purl/1123391 Publication ID: 31950

Michael Eldred, John Jakeman, Timothy Wildey, (2013). Deployment of Scalable UQ Methods for High-Fidelity Simulation-based Applications within the DOE https://www.osti.gov/servlets/purl/1673675 Publication ID: 36511

John Jakeman, Timothy Wildey, (2013). Scalable Uncertainty Quantification Methods https://www.osti.gov/servlets/purl/1666168 Publication ID: 34628

John Jakeman, Michael Eldred, (2013). Constructing Polynomial Chaos Expansions via Compressed Sensing and Cross Validation https://www.osti.gov/servlets/purl/1106456 Publication ID: 34737

John Jakeman, (2013). A Posteriori Error Analysis and Adaptive Construction of Surrogate Models https://www.osti.gov/servlets/purl/1080030 Publication ID: 33723

Andrew Salinger, Irina Tezaur, Mauro Perego, Raymond Tuminaro, Michael Eldred, John Jakeman, (2013). Rapid Development of an Ice Sheet Climate Application using the Components-Based Approach https://www.osti.gov/servlets/purl/1661056 Publication ID: 33356

John Jakeman, (2013). High-dimensional sparse grid interpolation and quadrature using one-dimensional Leja quadrature rules https://www.osti.gov/biblio/1073318 Publication ID: 32913

Michael Eldred, Stefan Domino, Matthew Barone, John Jakeman, (2013). Advances in UQ Algorithms for Wind Energy Applications https://www.osti.gov/biblio/1062946 Publication ID: 31642

John Jakeman, Timothy Wildey, (2013). Quantifying Uncertainty using a-posteriori Enhanced Sparse Grid Approximations https://www.osti.gov/biblio/1063316 Publication ID: 32169

John Jakeman, (2013). Constructing Polynomial Chaos Expansions via Compressed Sensing and Cross Validation https://www.osti.gov/biblio/1063474 Publication ID: 31245

Habib Najm, Khachik Sargsyan, Cosmin Safta, Bert Debusschere, John Jakeman, Michael Eldred, (2012). Sparse Polynomial Representations of High Dimensional Models https://www.osti.gov/biblio/1073443 Publication ID: 28932

John Jakeman, Timothy Wildey, Michael Eldred, (2012). Adaptive sparse grids for uncertainty quantication Enhancing approximations using a posteriori error estimation https://www.osti.gov/biblio/1073416 Publication ID: 29105

John Jakeman, (2012). A Discussion of Gaussian Process Models and Polynomial Chaos Methods for Uncertainty Quantification https://www.osti.gov/biblio/1073376 Publication ID: 28229

John Jakeman, (2012). Locally Adaptive Generalised Sparse Grids https://www.osti.gov/servlets/purl/1078654 Publication ID: 27513

Habib Najm, Bert Debusschere, Michael Eldred, Cosmin Safta, John Jakeman, (2012). Quantification of Uncertainty in Extreme Scale Computations (QUEST) https://www.osti.gov/servlets/purl/1078675 Publication ID: 27011

John Jakeman, (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