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

John Davis Jakeman, (2022). PyApprox: Enabling efficient model analysis https://www.osti.gov/search/identifier:1879614 Document ID: 1595646

Timothy Michael Wildey, Gianluca Geraci, Michael S. Eldred, John Davis Jakeman, Owen Davis, Teresa Portone, Tian Yu NMN Yen, Bryan William Reuter, Alex Gorodetsky, Ahmad Rushdi, Daniele Schiavazzi, Lauren Partin, (2022). Embedded uncertainty estimation for data-driven surrogates to enable trustworthy ML for UQ Machine Learning and Deep Learning Conference Document ID: 1573362

Rebekah Dale White, Bart G van Bloemen Waanders, John Davis Jakeman, Alen Alexanderian, Arvind Saibaba, (2022). Coupling optimal experimental design and optimal control World Congress on Computational Mechanics Document ID: 1562916

Rebekah Dale White, Bart G van Bloemen Waanders, John Davis Jakeman, (2022). Machine learning in the context of inverse, control, and experimental design problems Machine learning and deep learning — internal Sandia conference Document ID: 1562920

Gianluca Geraci, Michael S. Eldred, Alex Gorodetsky, John Davis Jakeman, Teresa Portone, Bryan William Reuter, (2022). Overview and perspectives on multifidelity UQ Inria Platon projet-team seminar Document ID: 1562979

Rebekah Dale White, Bart G van Bloemen Waanders, Drew Philip Kouri, John Davis Jakeman, Alen Alexanderian, (2022). Bayesian risk-averse optimal experimental design with feedback UT Austin working group Document ID: 1539077

Michael S. Eldred, Gianluca Geraci, Bryan William Reuter, Teresa Portone, John Davis Jakeman, Alex A. Gorodetsky, (2022). Model Tuning for Multifidelity Sampling in Dakota 8th European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS) Document ID: 1527920

Cosmin Safta, John Davis Jakeman, Alex Gorodetsky, (2022). Reverse-mode differentiation in arbitrary tensor network format: with application to supervised learning Journal of Machine Learning Research https://www.osti.gov/search/identifier:1872019 Document ID: 1527753

John Davis Jakeman, (2022). PyApprox: Approximation and Probabilistic Analysis of Data SIAM Conference on Uncertainty Quantification Document ID: 1504805

Daniel Thomas Seidl, John Davis Jakeman, (2022). Improving Digital Twins by Learning from a Fleet of Assets SIAM Conference on Uncertainty Quantification Document ID: 1505152

Teeratorn Kadeethum, John Davis Jakeman, Youngsoo Choi, Nikolaos Bouklas, Hongkyu Yoon, (2022). Barlow twins reduced order modeling with uncertainty quantification for contact problems USACM Thematic Conference on Uncertainty Quantification for Machine Learning Integrated Physics Modeling Document ID: 1494416

Michael S. Eldred, Gianluca Geraci, Teresa Portone, Alex A. Gorodetsky, John Davis Jakeman, (2022). All-at-Once (and Bi-Level) Model Tuning for Multifidelity Sampling SIAM Conference on Uncertainty Quantification (UQ22) Document ID: 1505114

Cosmin Safta, John Davis Jakeman, Khachik Sargsyan, (2022). Quantifying Uncertainty in E3SM via Functional Tensor Network Approximations Siam Uq22 Document ID: 1504983

Rebekah Dale White, Bart G van Bloemen Waanders, John Davis Jakeman, Alexanderian Alen, Drew Philip Kouri, (2022). Exploring risk-averse design criteria for sequential optimal experimental design in a Bayesian setting Siam Uq Document ID: 1504627

Lorenzo Tamellini, John Davis Jakeman, Irina Kalashnikova Tezaur, (2022). Machine Learning And Uncertainty Quantification For Coupled Multi-physics, Multi-scale And Multi-fidelity Modeling X International Conference on Computational Methods for Coupled Problems Document ID: 1494056

James Warner, Geoffrey Bomarito, Gianluca Geraci, Michael S. Eldred, Marten Thompson, John Davis Jakeman, Patrick Leser, Paul Leser, Alex Gorodetsky, (2022). Automating Model Selection and Tuning for Multifidelity UQ (MFUQ) Siam Uq 22 Document ID: 1494403

Irina Kalashnikova Tezaur, Kara J. Peterson, Amy Jo Powell, John Davis Jakeman, Erika Louise Roesler, (2022). Global Sensitivity Analysis Using the Ultra-Low Resolution Energy Exascale Earth System Model (E3SM) Siam Uq 2022 Document ID: 1493699

John Davis Jakeman, Sam Friedman, Michael S. Eldred, Lorenzo Tamellini, Alex Gorodestky, Doug Allaire, (2022). Adaptive experimental design for multi-fidelity surrogate modeling of multi-disciplinary systems International Journal For Numerical Methods In Engineering https://www.osti.gov/search/identifier:1855808 Document ID: 1481631

Qian Wang, Joseph Guillaume, John Davis Jakeman, Barry Croke, Tao Yang, Anthony Jakeman, (2022). Identifying metrics in adaptive evaluation of impacts of factor fixing on objective functions under model structure uncertainty 2022 International Environmental Modelling and Software Society Biennial Meeting Document ID: 1470968

John Davis Jakeman, Drew Philip Kouri, Jose Gabriel Huerta, (2022). Surrogate Modeling For Efficiently, Accurately and Conservatively Estimating Measures of Risk Reliability Engineering & System Safety https://www.osti.gov/search/identifier:1845389 Document ID: 1437752

Qian (Australian National University) Wang, Joseph (Australian National University) Guillaume, John Davis Jakeman, Tao(Hohai University) Yang, Takuya (Australian National University) Iwanaga, Barry (Australian National University) Croke, Tony (Australian National University) Jakeman, (2022). Assessing the predictive impact of factor fixing with an adaptive uncertainty-based approach Environmental Modelling & Software https://www.osti.gov/search/identifier:1845388 Document ID: 1437808

John Davis Jakeman, (2021). Multi-fidelity machine learning and uncertainty quantification with PyApprox The 8th European Congress on Computational Methods in Applied Sciences and Engineering Document ID: 1405571

Cosmin Safta, Khachik Sargsyan, John Davis Jakeman, (2021). Functional Tensor Network Approximations for E3SM Land Model AGU Fall Meeting 2021 Document ID: 1405759

Michael S. Eldred, Gianluca Geraci, Bryan William Reuter, Teresa Portone, John Davis Jakeman, Alex A. Gorodetsky, (2021). Model Tuning For Multifidelity Sampling In Dakota 8th European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS Congress 2022) Document ID: 1405437

Geoffrey F. Bomarito, Gianluca Geraci, James E. Warner, Patrick E. Leser, William P. Leser, Michael S. Eldred, John Davis Jakeman, Alex A. Gorodetsky, (2021). Improving Multi-Model Trajectory Simulation Estimators using Model Selection and Tuning AIAA SciTech 2021 Document ID: 1404299

Geoffrey F. Bomarito, Gianluca Geraci, James E. Warner, Patrick E. Leser, William P. Leser, Michael S. Eldred, John Davis Jakeman, Alex A. Gorodetsky, (2021). Improving Multi-Model Trajectory Simulation Estimators using Model Selection and Tuning AIAA SciTech 2021 Document ID: 1404320

Michael S. Eldred, Gianluca Geraci, Gorodetsky, John Davis Jakeman, (2021). Leveraging Multiple Information Sources to Enable High-Fidelity Uncertainty Quantification AE585 Chair’s Distinguished Lecture Series Document ID: 1404176

Daniel Thomas Seidl, John Davis Jakeman, (2021). Improving Digital Twins by Learning from a Fleet of Assets SIAM Conference on Uncertainty Quantification (UQ22) Document ID: 1369604

John Davis Jakeman, (2021). PyApprox: Approximation and probabilistic analysis of data Siam Conference on Uncertainty Quantification Document ID: 1369611

James Warner, Geoffrey Bomarito, Patrick Leser, William Leser, Gianluca Geraci, Michael S. Eldred, John Davis Jakeman, (2021). Automating Model Selection and Tuning for Multifidelity UQ SIAM Conference on Uncertainty Quantification (UQ22) Document ID: 1369650

Michael S. Eldred, Gianluca Geraci, Alex Arkady Gorodetsky, John Davis Jakeman, Teresa Portone, Timothy Michael Wildey, Ahmad Rushdi, Daniel Thomas Seidl, (2021). The Dakota Project: Connecting the Pipeline from Uncertainty Quantification R&D to Mission Impact NASA Langley HPC Seminar Series Document ID: 1369333

John Davis Jakeman, Drew Philip Kouri, Jose Gabriel Huerta, (2021). Surrogate Modeling For Efficiently, Accurately and Conservatively Estimating Measures of Risk Mechanistic Machine Learning and Digital Twins for Computational Science, Engineering & Technology Document ID: 1367943

Daniel Thomas Seidl, John Davis Jakeman, (2021). Improving Digital Twins by Learning from a Fleet of Assets Mechanistic Machine Learning and Digital Twins for Computational Science, Engineering & Technology Document ID: 1368070

Zachary Benjamin Morrow, Bart G van Bloemen Waanders, John Davis Jakeman, (2021). Characterizing Approximation Methods for Digital Twins in Scientific Computing Mechanistic Machine Learning and Digital Twins for Computational Science, Engineering & Technology Document ID: 1368243

Rebekah Dale White, John Davis Jakeman, Bart G van Bloemen Waanders, Drew Philip Kouri, Alex Alexanderian, (2021). Exploring risk-averse design criteria for sequential optimal experimental design in a Bayesian setting Mechanistic Machine Learning and Digital Twins for Compuational Science, Engineering & Technology Document ID: 1367990

Tong Qin, Zhen Chen, John Davis Jakeman, Dongbin Xiu, (2021). Data-driven learning of non-autonomous systems SIAM Journal on Scientific Computing Document ID: 1343631

Teresa Portone, John Davis Jakeman, Alex Arkady Gorodetsky, (2021). Advanced multilevel and multifidelity UQ strategies: applications, generalized model hierarchies, and data-driven approaches SIAM Conference of Uncertainty Quantification (UQ22) Document ID: 1367773

Drew Philip Kouri, John Davis Jakeman, Jose Gabriel Huerta, Timothy Walsh, Chandler Baldwin Smith, Stan Uryasev, (2021). Risk-Adaptive Experimental Design for High-Consequence Systems: LDRD Final Report https://www.osti.gov/search/identifier:1820307 Document ID: 1356826

Alex Arkady Gorodetsky, John Davis Jakeman, Gianluca Geraci, (2021). MFNets: data efficient all-at-once learning of multifidelity surrogates as directed networks of information sources Computational Mechanics https://www.osti.gov/search/identifier:1820427 Document ID: 1355844

Timothy Michael Wildey, Troy Butler, John Davis Jakeman, Anh Tran, (2021). Solving Stochastic Inverse Problems for Property-Structure Relationships in Computational Materials Science US National Congress on Computational Mechanics Document ID: 1342636

Michael S. Eldred, Gianluca Geraci, Alex Arkady Gorodetsky, John Davis Jakeman, Teresa Portone, (2021). Efficient Deployment of Multifidelity Sampling Methods in Production Settings Usnccm16 Document ID: 1342732

Xiaoshu Zeng, Gianluca Geraci, Michael S. Eldred, John Davis Jakeman, Alex Gorodetsky, Roger Ghanem, (2021). Adaptive Basis for Multifidelity Uncertainty Quantification 16th US National Congress on Computational Mechanics Document ID: 1332497

John Davis Jakeman, Michael S. Eldred, Gianluca Geraci, Teresa Portone, Ahmad Rushdi, Daniel Thomas Seidl, Thomas M. Smith, (2021). Multi-fidelity Machine Learning Machine Learning and Deep Learning Conference https://www.osti.gov/search/identifier:1876608 Document ID: 1331137

John Davis Jakeman, Samuel Friedman, Michael S. Eldred, Lorenzo Tamellini, Alex Gorodetsky, Doug Allaire, (2021). Adaptive resource allocation for surrogate modeling of systems comprised of multiple disciplines with varying fidelity IX International Conference on Coupled Problems in Science and Engineering https://www.osti.gov/search/identifier:1872879 Document ID: 1318770

John Davis Jakeman, Drew Philip Kouri, Jose Gabriel Huerta, (2021). Surrogate Modeling For Efficiently, Accurately and Conservatively Estimating Measures of Risk https://www.osti.gov/search/identifier:1807455 Document ID: 1318256

Sam Friedman, John Davis Jakeman, Michael S. Eldred, Lorenzo Tamellini, Alex Gorodestky, Doug Allaire, (2021). Adaptive resource allocation for surrogate modeling of systems comprised of multiple disciplines with varying fidelity https://www.osti.gov/search/identifier:1807453 Document ID: 1318164

William Mcneil Reese, Joseph Lee Hart, Bart G van Bloemen Waanders, Mauro Perergo, John Davis Jakeman, Arvind Saibaba, (2021). Bedrock Inversion and Hyper Differential Sensitivity Analysis for the Shallow Ice Model Numerical Analysis in Data Science Transition Workshop Document ID: 1317435

Timothy Michael Wildey, Troy Butler, John Davis Jakeman, (2021). Combining Measure Theory and Bayes? Rule to Solve a Stochastic Inverse Problem Emi/pmc 2021 https://www.osti.gov/search/identifier:1877851 Document ID: 1294038

Daniel Thomas Seidl, John Davis Jakeman, (2021). Digital Twin Modeling with Gaussian Process Networks Mechanistic Machine Learning and Digital Twins for Computational Science, Engineering & Technology Document ID: 1292810

John Davis Jakeman, Drew Philip Kouri, Jose Gabriel Huerta, (2021). Risk-Adapted Surrogate Modeling Mechanistic Machine Learning and Digital Twins for Computational Science, Engineering & Technology Document ID: 1292834

John Davis Jakeman, Alex Gorodetsky, Michael S. Eldred, Gianluca Geraci, Thomas M. Smith, (2021). MFNETS: Multi-Fidelity Data-Driven Networks for Data Analysis MFNETSMulti-Fidelity Data-Driven Networks for Data Analysis https://www.osti.gov/search/identifier:1854429 Document ID: 1279900

Cosmin Safta, Khachik Sargsyan, John Davis Jakeman, Alex Arkady Gorodetsky, (2021). Low-Rank Tensor Network Approximations for Earth System Model Siam Cse 2021 https://www.osti.gov/search/identifier:1854317 Document ID: 1280411

Bert Debusschere, Gianluca Geraci, John Davis Jakeman, Cosmin Safta, Laura Painton Swiler, (2021). Polynomial Chaos Expansions for Discrete Random Variables in Cyber Security Emulytics Experiments SIAM Computational Science and Engineering https://www.osti.gov/search/identifier:1847628 Document ID: 1279399

Michael S. Eldred, Alex Arkady Gorodetsky, Gianluca Geraci, John Davis Jakeman, Teresa Portone, (2021). Recent Advances in Adaptive Refinement of (Regression-Based) Multifidelity Surrogates for UQ Siam Cse21 https://www.osti.gov/search/identifier:1847573 Document ID: 1279875

Laura Painton Swiler, Mamikon Gulian, Ari Louis Frankel, Cosmin Safta, John Davis Jakeman, (2021). Constrained Gaussian Processes: A Survey SIAM Computational Science and Engineering Conference 2021 https://www.osti.gov/search/identifier:1847480 Document ID: 1268757

Michael S. Eldred, Gianluca Geraci, Alex Gorodetsky, John Davis Jakeman, (2021). Enhancing Multifidelity UQ with model tuning 16th U.S. National Congress on Computational Mechanics Document ID: 1267977

Xiaoshu Zeng, Gianluca Geraci, Michael S. Eldred, John Davis Jakeman, Alex Gorodetsky, Roger Ghanem, (2021). Adaptive Basis for Multifidelity Uncertainty Quantification 16th U.S. National Congress on Computational Mechanics Document ID: 1267957

Sam Freidman, John Davis Jakeman, Michael S. Eldred, Lorenzo Tamellini, Gorodetsky Alex, Doug Allaire, (2021). Greedy resource allocation for analysis of integrated system models IX International Conference on Coupled Problems in Science and Engineering Document ID: 1267548

Alex Gorodetsky, Gianluca Geraci, John Davis Jakeman, (2021). Data-enhanced Modeling and Uncertainty Quantification of Systems with Multiple Fidelities 16th U.S. National Congress on Computational Mechanics Document ID: 1255850

Saman Razavi, Anthony Jakeman, Andrea Saltelli, Bertrand Looss, Emanuele Borgonovo, Elmar Plischke, Samuele Lo Piano, Takuya Iwanaga, William Becker, Stefano Tarantola, Joseph Guillaume, John Davis Jakeman, Hoshin Gupta, Nicola Melillo, Giovani Rabitti, Vincent Chabridon, Qingyun Duan, Xifu Sun, Stefan 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://www.osti.gov/search/identifier:1760445 Document ID: 1244613

Alex Gorodetsky, Kazuya Tsuji, John Davis Jakeman, Gianluca Geraci, Michael S. Eldred, (2020). Multifidelity information fusion via network models for uncertainty quantification in aerospace dynamical systems AIAA SciTech 2021 https://www.osti.gov/search/identifier:1836910 Document ID: 1244394

Alex Gorodetsky, John Davis Jakeman, Gianluca Geraci, Michael S. Eldred, (2020). Data-driven Network Representations For Multifidelity Surrogate Modeling (mfnets) Uncecomp 21 Document ID: 1243335

Harbrecht Helmut, John Davis Jakeman, Peter Zaspel, (2020). Cholesky-based experimental design for Gaussian process and kernel-based emulation and calibration Communications in Computational Physics https://www.osti.gov/search/identifier:1770338 Document ID: 1230866

Baihua Fu, Jeffery Horsburgh, Anthony Jakeman, Carlo Gualtieri, Thorsten Arnold, Lucy Marshall, Tim Green, Nigel Quinn, Martin Volk, Randall Hunt, Luca Vezarro, Barry Croke, John Davis Jakeman, Val Snow, Brenda Rashleigh, (2020). Modeling water quality in freshwater systems: From here to the next generation Water resources research https://www.osti.gov/search/identifier:1760464 Document ID: 1230470

Laura Painton Swiler, Mamikon Gulian, Ari Louis Frankel, Cosmin Safta, John Davis Jakeman, (2020). A Survey of Constrained Gaussian Process Regression: Approaches and Implementation Challenges Journal of Machine Learning for Modeling and Computing https://www.osti.gov/search/identifier:1691455 Document ID: 1209606

Tong, Qin, Zhen Chen, John Davis Jakeman, Dongbin Xiu, (2020). Deep learning of parameterized equations with applications to uncertainty quantification International Journal for Uncertainty Quantification https://www.osti.gov/search/identifier:1738923 Document ID: 1197019

Alex Gorodetsky, John Davis Jakeman, Gianluca Geraci, Michael S. Eldred, (2020). Mfnets: Multi-fidelity Data-driven Networks For Bayesian Learning, Uncertainty Quantification, And Prediction 14th World Congress on Computational Mechanics (WCCM) Document ID: 1208334

Gianluca Geraci, Alex Gorodetsky, Michael S. Eldred, John Davis Jakeman, (2020). Multilevel/multifidelty Strategies For Uncertainty Quantification, Control And Design Under Uncertainty Of Expensive Computational Systems 14th World Congress on Computational Mechanics (WCCM) Document ID: 1208336

Bert Debusschere, Gianluca Geraci, John Davis Jakeman, Cosmin Safta, Laura Painton Swiler, (2020). Polynomial Chaos Expansions for Discrete Random Variables in Cyber Security Emulytics Experiments Siam Conference on Computational Science and Engineering Document ID: 1207574

Gianluca Geraci, Michael S. Eldred, Alex Gorodetsky, John Davis Jakeman, (2020). Multifidelity Strategies in UQ: an overview on some recent trends in sampling based approaches Doctoral Course ? Aeronautical and Space Engineering ? Summer School 2020, University of Rome La Sapienzame https://www.osti.gov/search/identifier:1822111 Document ID: 1207495

Kara J. Peterson, Amy Jo Powell, Irina Kalashnikova Tezaur, Erika Louise Roesler, Jeffrey Nichol, Matthew Gregor Peterson, Warren Leon Davis, John Davis Jakeman, David John Stracuzzi, Diana L Bull, (2020). Arctic Tipping Points Triggering Global Change LDRD Final Report https://www.osti.gov/search/identifier:1669210 Document ID: 1207328

Laura Painton Swiler, Mamikon Gulian, Ari Louis Frankel, John Davis Jakeman, Cosmin Safta, (2020). LDRD Project Summary: Incorporating physical constraints into Gaussian process surrogate models https://www.osti.gov/search/identifier:1668928 Document ID: 1196386

Bill Lozanovski, Downing David, Rance Tio, Anton du Plessis, Phuong Tran, John Davis Jakeman, Darpin Shadid, 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://www.osti.gov/search/identifier:1667431 Document ID: 1196734

John Davis Jakeman, Michael S. Eldred, Gianluca Geraci, Thomas M. Smith, Alex Gorodetsky, (2020). LDRD #218317: Learning Hidden Structure in Multi-Fidelity Information Sources for Efficient Uncertainty Quantification https://www.osti.gov/search/identifier:1668458 Document ID: 1196674

Michael S. Eldred, Alex Gorodetsky, Gianluca Geraci, John Davis Jakeman, Teresa Portone, (2020). Recent advances in adaptive refinement of multifidelity surrogates Siam Cse 2021 Document ID: 1196332

Laura Painton Swiler, Mamikon Gulian, Ari Louis Frankel, Cosmin Safta, John Davis Jakeman, (2020). Constrained Gaussian Processes: A Survey SIAM Computational Science and Engineering 2021 Document ID: 1196004

Mamikon Gulian, Laura Painton Swiler, Ari Louis Frankel, Cosmin Safta, John Davis Jakeman, (2020). A Survey of Constrained Gaussian Process Regression: Approaches and Implementation Challenges PhILMs Webinar https://www.osti.gov/search/identifier:1814448 Document ID: 1184835

Mamikon Gulian, Laura Painton Swiler, Ari Louis Frankel, John Davis Jakeman, Cosmin Safta, (2020). A Survey of Constrained Gaussian Process Regression: Approaches and Implementation Challenges Sandia Machine Learning and Deep Learning Workshop Skip to end of banner https://www.osti.gov/search/identifier:1812282 Document ID: 1183629

Cosmin Safta, Khachik Sargsyan, John Davis Jakeman, (2020). Low-Rank Tensor Network Approximations for Earth System Models AGU Fall Meeting 2020 Document ID: 1183448

Mamikon Gulian, Laura Painton Swiler, Ari Louis Frankel, Cosmin Safta, John Davis Jakeman, (2020). A Survey of Constrained Gaussian Process Regression: Approaches and Implementation Challenges Machine Learning and Deep Learning Conference (Sandia Audience) Document ID: 1162165

Alex Gorodetsky, John Davis Jakeman, Gianluca Geraci, Michael S. Eldred, (2020). MFNets: Multifidelity data-driven networks for Bayesian learning and prediction International Journal for Uncertainty Quantification https://www.osti.gov/search/identifier:1670735 Document ID: 1161876

Laura Painton Swiler, Mamikon Gulian, Ari Louis Frankel, Cosmin Safta, John Davis Jakeman, (2020). A Survey of Constrained Gaussian Process: Approaches and Implementation Challenges Journal of Machine Learning for Modeling and Computing https://www.osti.gov/search/identifier:1725870 Document ID: 1127442

Tong Qin, Zhen Chen, John Davis Jakeman, Dongbin Xiu, (2020). Data-driven learning of non-autonomous systems https://www.osti.gov/search/identifier:1763550 Document ID: 1139346

Keith Dalbey, Michael S. Eldred, Gianluca Geraci, John Davis Jakeman, Kathryn Anne Maupin, Jason A. Monschke, Daniel Thomas Seidl, Laura Painton Swiler, Anh Tran, Friedrich Menhorn, Xiaoshu Zeng, (2020). Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.12 Theory Manual https://www.osti.gov/search/identifier:1630693 Document ID: 1127943

Brian M. Adams, William J. Bohnhoff, Keith Dalbey, Mohamed Salah Ebeida, John P. Eddy, Michael S. Eldred, Russell Hooper, Patricia D. Hough, Kenneth Hu, John Davis Jakeman, Mohammad Khalil, Kathryn Anne Maupin, Jason A. Monschke, Elliott Marshall Ridgway, Ahmad Rushdi, Daniel Thomas Seidl, John Adam Stephens, Laura Painton Swiler, Justin Winokur, (2020). Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.12 User?s Manual https://www.osti.gov/search/identifier:1630694 Document ID: 1127946

Timothy Michael Wildey, Troy Butler, John Davis Jakeman, (2020). Optimal Experimental Design for Prediction Based on Push-forward Probability Measures Journal of Computational Physics https://www.osti.gov/search/identifier:1630281 Document ID: 1127990

Harbrecht Helmut, John Davis Jakeman, Peter Zaspel, (2020). Weighted greedy-optimal design of computer experiments for kernel-based and Gaussian process model emulation and calibration https://www.osti.gov/search/identifier:1608084 Document ID: 1115016

Alex Gorodetsky, Gianluca Geraci, Michael S. Eldred, John Davis Jakeman, (2020). A Generalized Approximate Control Variate Framework For Multifidelity Uncertainty Quantification Journal of Computational Physics https://www.osti.gov/search/identifier:1601260 Document ID: 1090543

Gianluca Geraci, Xiaoshu Zeng, Alex Gorodetsky, Michael S. Eldred, John Davis Jakeman, Roger Ghanem, (2020). Uncertainty Quantification With Multifidelity Strategies Based On Models With Dissimilar Parameterizations 14th World Congress on Computational Mechanics (WCCM) Document ID: 1079630

Cosmin Safta, John Davis Jakeman, Khachik Sargsyan, Alex Arkady Gorodetsky, (2020). Low-rank Approximations for High-dimensional and Computationally Expensive Models World Congress on Computational Mechanics Document ID: 1079525

Cosmin Safta, Khachik Sargsyan, John Davis Jakeman, (2019). Uncertainty Quantification for E3SM Land Component using Low-Rank Surrogate Models AGU Fall Meeting https://www.osti.gov/search/identifier:1643449 Document ID: 1067732

John Davis Jakeman, (2019). Uncertainty Quantification: An Overview Uncertainty Quantification Workshop https://www.osti.gov/search/identifier:1643293 Document ID: 1055856

Timothy Michael Wildey, Lukas Bruder, Tan Bui-Thanh, Troy Butler, John Davis Jakeman, Brad Marvin, Anh Tran, Scott Walsh, (2019). Moving Beyond Forward Simulation to Enable Data-informed Physics-based Predictions Colloquium at University of Colorado – Denver https://www.osti.gov/search/identifier:1646273 Document ID: 1055964

John Davis Jakeman, Michael-01463 Eldred, G-01463 Geraci, A Gorodetsky, (2019). Adaptive multi-index collocation for uncertainty quantification and sensitivity analysis https://www.osti.gov/search/identifier:1574406 Document ID: 1010142

Gianluca Geraci, Michael S. Eldred, Alex Gorodetsky, John Davis Jakeman, (2019). Recent advancement in Multifidelity Uncertainty Quantification NATO/STO Lecture SeriesUncertainty Quantification in Computational Fluid Dynamics https://www.osti.gov/search/identifier:1642820 Document ID: 1033397

Michael S. Eldred, Gianluca Geraci, Alex Arkady Gorodetsky, John Davis Jakeman, (2019). Multilevel / Multifidelity Sampling and Emulation for Forward UQ Applied Math Visioning Workshop https://www.osti.gov/search/identifier:1645988 Document ID: 1021104

Timothy Michael Wildey, Troy Butler, John Davis Jakeman, (2019). Convergence of Probability Densities using Approximate Models for Forward and Inverse Problems in Uncertainty Quantification AMS Fall Southeastern Sectional Meeting https://www.osti.gov/search/identifier:1641989 Document ID: 1020264

Cosmin Safta, Khachik Sargsyan, John Davis Jakeman, Alex Arkady Gorodetsky, (2019). Uncertainty Quantification for E3SM Land Component using Low-Rank Surrogate Models American Geophysical Society Fall Meeting 2019 Document ID: 997508

Gianluca Geraci, Michael S. Eldred, Alex Arkady Gorodetsky, John Davis Jakeman, (2019). Recent Advancements for Multifidelity UQ and OUU in Dakota: Capability Overview and Perspectives US National Congress of Computational Mechanics https://www.osti.gov/search/identifier:1641419 Document ID: 997237

Michael S. Eldred, Gianluca Geraci, Alex Arkady Gorodetsky, John Davis Jakeman, (2019). Experience with Multilevel/Multifidelity/Multi-Index Sampling and Surrogate Approaches for Forward Uncertainty Quantification 15th US National Congress on Computational Mechanics (USNCCM) https://www.osti.gov/search/identifier:1641388 Document ID: 997103

Cosmin Safta, Timothy Reid, John Davis Jakeman, Khachik Sargsyan, (2019). Approximating Data with Stochastic and Physical Dependence using the Functional Tensor Train Models Poster?s on the Patio https://www.osti.gov/search/identifier:1641238 Document ID: 986105

Tiernan Albert Casey, Bert Debusschere, Michael S. Eldred, Gianluca Geraci, Roger Ghanem, John Davis Jakeman, Youssef Marzouk, Habib N. Najm, Cosmin Safta, Khachik Sargsyan, (2019). FASTMath: UQ Algorithms SciDAC PI Meeting 2019 https://www.osti.gov/search/identifier:1641088 Document ID: 985493

Timothy Michael Wildey, Troy Butler, John Davis Jakeman, Lukas Bruder, (2019). Solving Stochastic Inverse Problems using Approximate Push-forward Densities based on a Multi-fidelity Monte Carlo Method 9th International Congress on Industrial and Applied Mathematics https://www.osti.gov/search/identifier:1641047 Document ID: 985120

Cosmin Safta, Khachik Sargsyan, John Davis Jakeman, Alex Arkady Gorodetsky, Daniel Ricciuto, (2019). Exploiting Model Structure for Forward Propagation of Uncertainty in Earth System Models 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering https://www.osti.gov/search/identifier:1640926 Document ID: 974364

Cosmin Safta, John Davis Jakeman, Alex Arkady Gorodetsky, (2019). Low-Rank Functional Tensor Train Representations for High-Dimensional Computational Models FASTMath-4 Institute – All-Hands Meeting https://www.osti.gov/search/identifier:1645344 Document ID: 973303

Luca Bertagna, John Davis Jakeman, Mauro Perego, Irina Kalashnikova Tezaur, Jerry Watkins, Andrew G. Salinger, Xylar Asay-Davis, Matthew Hoffman, Stephen Price, Tong Zhang, Georg Stadler, (2019). Modeling Ice Sheets with MALI Semianar at ETH https://www.osti.gov/search/identifier:1645332 Document ID: 972669

John Davis Jakeman, (2019). Python Approximation Toolbox (PyApprox) v. 1.0 Document ID: 961723

John Davis Jakeman, (2019). A mathematical perspective on the certification and design of physical systems in the presence of uncertainty CCR Seminar Series https://www.osti.gov/search/identifier:1645241 Document ID: 902576

John Davis Jakeman, Fabian Franzelin, Akil Narayan, Michael S. Eldred, Dirk Pflueger, (2019). Polynomial chaos expansions for dependent random variables https://www.osti.gov/search/identifier:1762354 Document ID: 936669

Gianluca Geraci, Alex Gorodetsky, Michael S. Eldred, John Davis Jakeman, (2019). Recent advancements toward generalized sampling strategies for multifidelity Uncertainty Quantification Workshop on ‘Uncertainty Quantification for nonlinear problems and applications in porous media’ organized by NORCE Norwegian Research Centre https://www.osti.gov/search/identifier:1644568 Document ID: 937201

Cosmin Safta, Khachik Sargsyan, John Davis Jakeman, Alex Gorodetsky, Daniel Ricciuto, (2019). Exploiting Low-Rank Structure for Sensitivity Analysis in Earth System Models SIAM Computational Science and Engineering https://www.osti.gov/search/identifier:1639271 Document ID: 936073

Mauro Perego, John Davis Jakeman, William Mark Severa, Lars Ruthotto, (2019). Neural Networks Surrogates of PDE-based Dynamical Systems: Application to Ice Sheet Dynamics Siam CSE 2019 https://www.osti.gov/search/identifier:1639251 Document ID: 935904

Timothy Michael Wildey, Troy Butler, John Davis Jakeman, Tan Bui-Thanh, Brad Marvin, Lukas Bruder, (2019). Developing Scalable and Multi-fidelity Approaches for Push-forward Based Inference DOE ASCR Applied Mathematics PI Meeting https://www.osti.gov/search/identifier:1596420 Document ID: 913330

Gianluca Geraci, Michael S. Eldred, Alex Gorodetsky, John Davis Jakeman, (2019). Recent advancements in Multilevel-Multifidelity techniques for forward UQ in the DARPA Sequoia project AIAA SciTech Forum 2019 https://www.osti.gov/search/identifier:1582124 Document ID: 901672

Cosmin Safta, Dan Ricciuto, Alex Gorodetsky, John Davis Jakeman, (2018). Exploiting Model Structure for Global Sensitivity Analysis in E3SM Land Model AGU Fall Meeting https://www.osti.gov/search/identifier:1761160 Document ID: 901039

Alex Gorodetsky (University of Michigan), John Davis Jakeman, (2018). Gradient-based Optimization for Regression in the Functional Tensor-Train Format Journal of Computational Physics https://www.osti.gov/search/identifier:1485822 Document ID: 865455

John Davis Jakeman, Akil Narayan (University of Utah), (2018). Generation and application of multivariate polynomial quadrature rules Computer Methods in Applied Mechanics and Engineering https://www.osti.gov/search/identifier:1485818 Document ID: 865457

Gianluca Geraci, Michael S. Eldred, John Davis Jakeman, (2018). Approximate Control Variates arXiv Document ID: 888903

Timothy Michael Wildey, Troy Butler, John Davis Jakeman, (2018). The Consistent Bayesian Approach for Stochastic Inverse Problems AMS Fall Southeastern Sectional Meeting https://www.osti.gov/search/identifier:1592669 Document ID: 888504

Michael S. Eldred, Gianluca Geraci, Alex A. Gorodetsky, John Davis Jakeman, (2018). Multilevel-Multifidelity Sampling and Emulation for Forward UQ Workshop IIHPC and Data Science for Scientific Discovery Document ID: 878063

John Davis Jakeman, Mauro Perego, William Mark Severa, (2018). Neural Networks as Surrogates of Nonlinear High-Dimensional Parameter-to-Prediction Maps https://www.osti.gov/search/identifier:1531317 Document ID: 875764

Cosmin Safta, Khachik Sargsyan, John Davis Jakeman, (2018). Exploiting Model Structure for Global Sensitivity Analysis in E3SM Land Model American Geophysical Society 2018 Fall Meeting Document ID: 842281

Joseph H.A., Guillaume, John Davis Jakeman, Stefano Marsili-Libelli, Michael Asher, Philip Brunner, Barry Croke, Mary C. Hill, Anthony J. Jakeman, Karel J. Keesman, Johannes D. Stigter, (2018). Acknowledging critical sources of uncertainty; Introductory Overview of Identifiability analysis Environmental Modeling and Software Document ID: 841810

John Davis Jakeman, Roland Pulch (University of Greifswald), (2018). Time and Frequency Domain Methods for Basis Selection in Random Linear Dynamical Systems International Journal of Uncertainty Quantification https://www.osti.gov/search/identifier:1496998 Document ID: 853884

Ben Adcock (Simon Fraser University), Anyi Bao (Simon Fraser University), John Davis Jakeman, Akil Naryan (University of Utah), (2018). Compressed sensing with sparse corruptions: Fault-tolerant sparse collocation approximations SIAM Journal on Scientific Computing https://www.osti.gov/search/identifier:1479490 Document ID: 865460

Michael S. Eldred, Gianluca Geraci, Alex Gorodetsky, John Davis Jakeman, (2018). Lecture 1: Multilevel-Multifidelity with Monte Carlo Sampling; Algorithms and deployment experience Uncertainty Quantification Summer School https://www.osti.gov/search/identifier:1582192 Document ID: 842780

Michael S. Eldred, Gianluca Geraci, Alex Gorodetsky, John Davis Jakeman, (2018). Lecture 3: Multilevel-Multifidelity Optimization; Deterministic Design and Design Under Uncertainty Uncertainty Quantification Summer School Document ID: 842782

Michael S. Eldred, Gianluca Geraci, Alex Gorodetsky, John Davis Jakeman, (2018). Lecture 2: Multilevel-Multifidelity beyond Monte Carlo; Polynomial chaos and Stochastic collocation Uncertainty Quantification Summer School Document ID: 842781

John Davis Jakeman, Troy Butler (University of Colorado Denver), Michael S. Eldred, Gianluca Geraci, Alex Gorodetsky (University of Michigan), Timothy Michael Wildey, (2018). Adaptive multi-index collocation for quantifying uncertainty 5th Workshop on Sparse Grids and Applications https://www.osti.gov/search/identifier:1806541 Document ID: 831137

John Davis Jakeman, Akil (University of Utah) Narayan, Fabian (University of Stuttgart) Franzelin, (2018). Polynomial Chaos Expansions for dependent random variables SIAM Conference on Uncertainty Quantification https://www.osti.gov/search/identifier:1570817 Document ID: 784877

Timothy Michael Wildey, Troy Butler, John Davis Jakeman, Brad Marvin, (2018). Consistent Bayesian Inference with Push-forward Measures: Scalable Implementations and Applications SIAM Annual Meeting https://www.osti.gov/search/identifier:1567818 Document ID: 830217

Troy Butler, John Davis Jakeman, Timothy Michael Wildey, (2018). Convergence of Probability Densities using Approximate Models for Forward and Inverse Problems in Uncertainty Quantification SIAM Journal on Scientific Computing https://www.osti.gov/search/identifier:1479491 Document ID: 819775

Timothy Michael Wildey, Troy Butler, John Davis Jakeman, (2018). Combining Measure Theory and Bayes Rule to Solve a Stochastic Inverse Problem Center for Computing Research, Summer Seminar Series Document ID: 808781

Timothy Michael Wildey, Troy Butler, John Davis Jakeman, Daniel Thomas Seidl, Bart G van Bloemen Waanders, (2018). Data-informed Multiscale Modeling of Additive Materials Engineering Mechanics Institute Conference 2018 https://www.osti.gov/search/identifier:1523778 Document ID: 807805

John Davis Jakeman, Mauro Perego, Irina Kalashnikova Tezaur, Stephen (LANL) Price, Georg (NYU) Stadler, (2018). Ice Sheet Initialization and Uncertainty Quantification of SeaLevel Rise 6th European Seminar on Computing https://www.osti.gov/search/identifier:1523714 Document ID: 807786

Mauro Perego, Luca Bertagna, Matthew (LANL) Hoffman, John Davis Jakeman, Stephen (LANL) Price, Andrew G. Salinger, Georg (NYU) Stadler, Irina Kalashnikova Tezaur, Jerry Watkins, (2018). Ice Sheet Modeling: Computational and Mathematical Challenges Invited Talk at UNM Department of Mathematics and Statistics https://www.osti.gov/search/identifier:1513472 Document ID: 795725

Gianluca Geraci, Alex Gorodetsky, Michael S. Eldred, John Davis Jakeman, (2018). Towards Leveraging Active Direction For Efficient Multifidelity Uq Strategies 7th European Conference on Computational Fluid Dynamics (ECFD 7) https://www.osti.gov/search/identifier:1525631 Document ID: 795451

Mauro Perego, John Davis Jakeman, Mauro Perego, Irina Kalashnikova Tezaur, Stephen (LANL) Price, Georg (NYU) Stadler, (2018). Methodologies for Enabling Bayesian Calibration in Landice Modeling Towards Probabilistic Projections of Sealevel Change SIAM Conference on Uncertainty Quantification https://www.osti.gov/search/identifier:1510847 Document ID: 795259

Michael S. Eldred, Gianluca Geraci, Alex A. Gorodetsky, John Davis Jakeman, (2018). Adaptive Refinement Strategies for Multilevel Polynomial Chaos Expansions SIAM Conference on Uncertainty Quantification (UQ18) https://www.osti.gov/search/identifier:1575179 Document ID: 784845

Ben Adcock (Simon Fraser University), Anyi Bao (Simon Fraser University), John Davis Jakeman, Akil Naryan (University of Utah), (2018). Compressed sensing with sparse corruptions: Fault-tolerant sparse collocation approximations https://www.osti.gov/search/identifier:1434573 Document ID: 626129

Timothy Michael Wildey, Troy Butler, John Davis Jakeman, Scott Walsh, (2018). Optimal Experimental Design for Prediction Using a Consistent Bayesian Approach SIAM Conference on Uncertainty Quantification https://www.osti.gov/search/identifier:1507835 Document ID: 784746

Cosmin Safta, John Davis Jakeman, Roger Ghanem, (2018). Scalable Uncertainty Quantification: Exploiting Structure in Models and Data FASTMath Institute kick-off meeting https://www.osti.gov/search/identifier:1497534 Document ID: 761357

Gianluca Geraci, Alex Gorodetsky, John Davis Jakeman, Michael S. Eldred, (2018). SAMPLING-BASED MULTILEVEL/MULTIFIDELITY UNCERTAINTY QUANTIFICATION for Computational Fluid Dynamics applications Eccomas Eccm-ecfd 2018 Document ID: 750177

Alex Arkady Gorodetsky, John Davis Jakeman, (2018). Gradient-based Optimization for Regression in the Functional Tensor-Train Format https://www.osti.gov/search/identifier:1733296 Document ID: 727732

Alex Gorodetsky, Gianluca Geraci, Michael S. Eldred, John Davis Jakeman, (2018). Latent Variable Networks For Multifidelity Uncertainty Quantification And Data Fusion Eccomas Eccm-ecfd 2018 Document ID: 749951

Kara J. Peterson, Michael L. Parks, Eric Ackerman, Ray Bambha, Diana L Bull, Jennifer M Frederick, Jasper O. E. Hardesty, Anastasia Gennadyevna Ilgen, John Davis Jakeman, Amy Jo Powell, Matthew Gregor Peterson, Erika Louise Roesler, Cosmin Safta, David John Stracuzzi, Irina Kalashnikova Tezaur, (2018). Arctic Tipping Points Triggering Global Change Geoscience External Advisory Board Meeting https://www.osti.gov/search/identifier:1513640 Document ID: 749594

Alex Gorodetsky, Gianluca Geraci, Michael S. Eldred, John Davis Jakeman, (2018). Multifidelity Model Management using Latent Variable Bayesian Networks 2nd Physics Informed Machine Learning https://www.osti.gov/search/identifier:1513639 Document ID: 749486

Michael S. Eldred, Gianluca Geraci, Alex Gorodetsky, John Davis Jakeman, (2018). Multilevel-Multifidelity Approaches for Forward UQ in the DARPA SEQUOIA Project 2018 AIAA SciTech Forum and Exposition https://www.osti.gov/search/identifier:1513488 Document ID: 738567

Gianluca Geraci, Alex Gorodetsky, Michael S. Eldred, John Davis Jakeman, (2018). Multilevel and Multifidelity approaches for Uncertainty Quantification 13th World Congress in Computational Mechanics Document ID: 739249

Alex Gorodetsky, Gianluca Geraci, Michael S. Eldred, John Davis Jakeman, (2018). Multifidelity model management using latent variable Bayesian networks 13th World Congress in Computational Mechanics Document ID: 739250

Kara J. Peterson, Michael L. Parks, Eric Ackerman, Ray Bambha, Diana L Bull, Jennifer M Frederick, Jasper O. E. Hardesty, Anastasia Gennadyevna Ilgen, John Davis Jakeman, Matthew Gregor Peterson, Amy Jo Powell, Erika Louise Roesler, Cosmin Safta, David John Stracuzzi, Irina Kalashnikova Tezaur, (2017). Arctic Tipping Points Triggering Global Change Geoscience External Review Board Document ID: 726774

John Davis Jakeman, Akil Narayan (University of Utah), (2017). Generation and application of multivariate polynomial quadrature rules https://www.osti.gov/search/identifier:1510651 Document ID: 672434

John Davis Jakeman, Mauro Perego, Irina Kalashnikova Tezaur, Steve Price (LANL), (2017). Towards probabilistic predictions of future sea-level Forum "Math-for-Industry" https://www.osti.gov/search/identifier:1481488 Document ID: 714491

Anthony J. Jakeman (Australian National University), John Davis Jakeman, (2017). An overview of methods to identify and manage uncertainty for modelling problems in the water-environment-agriculture cross-sector Springer seriesMathematics for Industry – Agriculture as a metaphor for creativity in all human endeavours https://www.osti.gov/search/identifier:1429841 Document ID: 670880

Timothy Michael Wildey, Troy Butler, John Davis Jakeman, (2017). A Consistent Bayesian Approach for Solving Stochastic Inverse Problems ASCR Applied Math PI Meeting https://www.osti.gov/search/identifier:1469097 Document ID: 671501

Timothy Michael Wildey, John Davis Jakeman, Troy Butler, (2017). Advancing Beyond Interpretive Simulation to Inference for Prediction ASCR Applied Math PI Meeting https://www.osti.gov/search/identifier:1467988 Document ID: 670692

John Davis Jakeman, Alex Arkady Gorodetsky, Michael S. Eldred, (2017). Tractable Uncertainty Quantification: Exploiting Structure Sandia CIS Review https://www.osti.gov/search/identifier:1466103 Document ID: 659820

Michael S. Eldred, Gianluca Geraci, Alex Arkady Gorodetsky, John Davis Jakeman, (2017). Multilevel-Multifidelity Expansions with Application to Forward UQ, OUU, and Emulator-Based Bayesian Inference 14th U.S. National Congress on Computational Mechanics (USNCCM14) https://www.osti.gov/search/identifier:1507501 Document ID: 638105

Gianluca Geraci, Alex Arkady Gorodetsky, John Davis Jakeman, Michael S. Eldred, (2017). Sampling, Polynomial Chaos and Functional Tensor Train Multilevel/Multifidelity Strategies for Forward UQ SIAM Annual 17 https://www.osti.gov/search/identifier:1507076 Document ID: 638004

Irina Kalashnikova Tezaur, John Davis Jakeman, Michael S. Eldred, Mauro Perego, Stephen (LANL) Price, Andrew G. Salinger, (2017). Large-scale Deterministic Inversion and Bayesian Calibration in Land-Ice Modeling 14th U.S. National Congress on Computational Mechanics (USNCCM14) https://www.osti.gov/search/identifier:1460158 Document ID: 637178

Michael S. Eldred, Jason A. Monschke, John Davis Jakeman, Gianluca Geraci, (2017). Multilevel-Multifidelity Approaches for Uncertainty Quantification and Design Siam Cse 2017 https://www.osti.gov/search/identifier:1455372 Document ID: 599563

Scott Walsh (UC Denver), Timothy Michael Wildey, John Davis Jakeman, (2017). OptimalExperimental Design Using A Consistent Bayesian Approach ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part BMechanical Engineering https://www.osti.gov/search/identifier:1478379 Document ID: 623516

Timothy Michael Wildey, Troy Butler, John Davis Jakeman, Scott Walsh, (2017). A Consistent Bayesian Approach for Stochastic Inverse Problems Based on Push-forward Measures CCM Seminar – Department of Mathematical and Statistical Science – UC-Denver Document ID: 611679

Timothy Michael Wildey, Troy Butler, John Davis Jakeman, (2017). A Consistent Bayesian Formulation for Stochastic Inverse Problems Based on Push-forward Measures SIAM Journal on Scientific Computing https://www.osti.gov/search/identifier:1469654 Document ID: 611451

John Davis Jakeman, (2017). Multivariate Quadrature Rules for Correlated Random Variables SIAM Conference on Computational Science and Engineering. https://www.osti.gov/search/identifier:1427962 Document ID: 589389

Alex Arkady Gorodetsky, John Davis Jakeman, (2017). High-dimensional regression of low-rank functions Siam Cse https://www.osti.gov/search/identifier:1426383 Document ID: 599490

Anyi Bao (Simon Fraser University), Ben Adcock (Simon Fraser University), John Davis Jakeman, Akil Narayan (University of Utah), (2017). Compressive Sampling in Multivariate Polynomial Approximation with Corrupted Simulation Samples SIAM Conference on Computational Science and Engineering https://www.osti.gov/search/identifier:1424875 Document ID: 599458

Timothy Michael Wildey, John Davis Jakeman, Troy Butler, (2017). Efficient Sampling Strategies for the Consistent Bayesian Approach for Solving Stochastic Inverse Problems SIAM Conference on Computational Science and Engineering https://www.osti.gov/search/identifier:1425298 Document ID: 589385

Irina Kalashnikova Tezaur, John Davis Jakeman, Mauro Perego, Stephen (LANL) Price, (2017). Large-scale deterministic inversion and Bayesian calibration in land-ice modeling 14th U.S. National Congress on Computational Mechanics (USNCCM14) Document ID: 578699

Irina Kalashnikova Tezaur, Andrew G. Salinger, Mauro Perego, Raymond S. Tuminaro, John Davis Jakeman, Michael S. Eldred, Jerry Watkins, Stephen (LANL) Price, Irina (LANL) Demeshko, (2017). The Albany/FELIX Land-Ice Dynamical Core Albany User Group Meeting https://www.osti.gov/search/identifier:1416697 Document ID: 566474

Timothy Michael Wildey, John Davis Jakeman, Troy Butler, (2016). A Consistent Bayesian Approach for Stochastic Inverse Problems ICES Seminar Document ID: 531497

Timothy Michael Wildey, John Davis Jakeman, Troy Butler, (2016). A Consistent Bayesian Approach for Stochastic Inverse Problems European Congress on Computational Methods in Applied Sciences and Engineering https://www.osti.gov/search/identifier:1368940 Document ID: 464115

John Davis Jakeman, (2016). Compressed sensing and its role in designing aircraft nozzles in the presence of uncertainty Australian National University Computational Mathematics Seminar https://www.osti.gov/search/identifier:1365225 Document ID: 431805

John Davis Jakeman, Akil (University of Utah) Narayan, Tao Zhou (Chinese Academy of Sciences), (2016). Efficient Sampling Schemes for Recovering Sparse PCE SIAM confernce on uncertainty quantification https://www.osti.gov/search/identifier:1365093 Document ID: 430884

Mauro Perego, S. Price, G. Stadler, Andrew G. Salinger, Irina Kalashnikova Tezaur, Michael S. Eldred, John Davis Jakeman, (2016). Towards Uncertainty Quantification in 21st Century SeaLevel Rise Predictions: PDE Constrained Optimization as a First Step in Bayesian Calibration and Forward Propagation Siam Uq16 https://www.osti.gov/search/identifier:1366599 Document ID: 430780

Mauro Perego, John Davis Jakeman, S. Price, Andrew G. Salinger, G. Stadler, Irina Kalashnikova Tezaur, (2016). Computational Challenges in Ice Sheet Modeling Epfl https://www.osti.gov/search/identifier:1366600 Document ID: 430782

Irina Kalashnikova Tezaur, John Davis Jakeman, Michael S. Eldred, Mauro Perego, Andrew G. Salinger, Stephen (LANL) 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 SIAM Conference on Uncertainty Quantification https://www.osti.gov/search/identifier:1364846 Document ID: 430546

John Davis Jakeman, Akil Narayan (University of Utah), Tao Zhou (Institute of Computational Mathematics and the Chinese Academy of Sciences), (2016). A generalized sampling and preconditioning scheme for sparse approximation of polynomial chaos expansions SIAM Journal on Scientific Computing https://www.osti.gov/search/identifier:1375028 Document ID: 408793

Mauro Perego, Michael S. Eldred, John Davis Jakeman, Andrew G. Salinger, Irina Kalashnikova Tezaur, Stephen (LANL) Price, Matthew (LANL) Hoffman, (2016). Towards quantifying uncertainty in Greenland’s contribution to 21st century sea-level rise 2015 AGU fall meeting https://www.osti.gov/search/identifier:1339212 Document ID: 366048

Ahmad Rushdi, Laura Painton Swiler, Scott A. Mitchell, John Davis Jakeman, Eric T. Phipps, Mohamed Salah Ebeida, (2016). VPS: Voronoi Piecewise Surrogate Models for High-Dimensional Data Fitting Kaust https://www.osti.gov/search/identifier:1514295 Document ID: 376352

Michael Asher (Australian National University), John Davis Jakeman, Anthony Jakeman (Australian National University), (2015). Multifidelity Surrogates of Groundwater Flow American Geophysical Union Fall Meeting https://www.osti.gov/search/identifier:1503957 Document ID: 366055

Timothy Michael Wildey, John Davis Jakeman, (2015). Adaptive Bayesian Inference for Prediction https://www.osti.gov/search/identifier:1221574 Document ID: 321653

John Davis Jakeman, Yi Chen (Utah), Dongbin Xiu (Utah), Claude Gittelson, (2015). Dimension reduction for PDE using local Karhunen Loeve expansions https://www.osti.gov/search/identifier:1221524 Document ID: 341668

Timothy Michael Wildey, John N. Shadid, Eric Christopher Cyr, John Davis Jakeman, Troy Butler, (2015). Adjoint-Based a Posteriori Error Estimation and Uncertainty Quantification for Transient Nonlinear Problems with Discontinuous Solutions Numerical Methods for Large-Scale Nonlinear Problems and Their Applications https://www.osti.gov/search/identifier:1323036 Document ID: 320824

John Davis Jakeman, (2015). Multi-Variate Weighted Leja Sequences for Polynomial Approximation and UQ US National Congress on Computational Mechanics https://www.osti.gov/search/identifier:1290921 Document ID: 318860

Irina Kalashnikova Tezaur, Andrew G. Salinger, Mauro Perego, John Davis Jakeman, Michael S. Eldred, Irina Demeshko, Raymond S. Tuminaro, Stephen (LANL) Price, (2015). Albany/FELIX: A Robust & Scalable Trilinos-Based Finite-Element Ice Flow Dycore Built for Advanced Architectures & Analysis International Conference on Industrial and Applied Mathematics (ICIAM) 2015 https://www.osti.gov/search/identifier:1301963 Document ID: 319387

Timothy Michael Wildey, John Davis Jakeman, Troy Butler, Eric Christopher Cyr, John N. Shadid, (2015). Adjoint-Based a Posteriori Error Estimation and Uncertainty Quantification for Shock-Hydrodynamic Applications US National Congress on Computational Mechanics https://www.osti.gov/search/identifier:1279685 Document ID: 318828

Bert Debusschere, John Davis Jakeman, Kamaljit Singh 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 2015 SciDAC-3 PI Meeting https://www.osti.gov/search/identifier:1328212 Document ID: 318585

Michael S. Eldred, Bert Debusschere, Kamaljit Singh Chowdhary, John Davis Jakeman, Prashant Rai, Cosmin Safta, Khachik Sargsyan, (2015). Sandia Software Enabling Extreme-Scale Uncertainty Quantification 2015 SciDAC PI Meeting https://www.osti.gov/search/identifier:1266821 Document ID: 308277

Irina Kalashnikova Tezaur, Mauro Perego, Raymond S. Tuminaro, Andrew G. Salinger, John Davis Jakeman, Michael S. Eldred, Lili (SC) Ju, Tong (SC) Zhang, Max (FSU) Gunzburger, Stephen (LANL) Price, (2015). Progress on the PISCEES FELIX Ice Sheet Dynamical Cores SciDAC Principal Investigators Meetin https://www.osti.gov/search/identifier:1576124 Document ID: 308302

Timothy Michael Wildey, John Davis Jakeman, Troy Butler, (2015). Utilizing Adjoint-based Error Estimates to Adaptively Resolve Response Surface Approximations International Conference on Adaptive Modeling and Simulation https://www.osti.gov/search/identifier:1256570 Document ID: 286409

John Davis Jakeman, (2015). Sampling and Preconditioning Strategies for $\ell_1$-minimization SIAM Conference on Computational Science and Engineering https://www.osti.gov/search/identifier:1253294 Document ID: 232574

Michael S. Eldred, Patrick (MIT) Heimbach, Charles (UT Austin) Jackson, John Davis Jakeman, Mauro Perego, Stephen Price, Andrew G. Salinger, Georg (Courant) Stadler, Irina Kalashnikova Tezaur, (2015). From Deterministic Inversion to Uncertainty Quantification: Planning a Long Journey in Ice Sheet Modeling QUEST Workshop 2015 https://www.osti.gov/search/identifier:1246877 Document ID: 243502

Mauro Perego, Stephen (LANL) Price, Georg (Curant) Stadler, Michael S. Eldred, Charles (UT Austin) Jackson, John Davis Jakeman, Andrew G. Salinger, Irina Kalashnikova Tezaur, (2015). Advances in Ice Sheet Model Initialization Using the First Order Model Siam CSE 2015 https://www.osti.gov/search/identifier:1245907 Document ID: 232557

John Davis Jakeman, Yi Chen, Claude Gittelson, Dongbin Xiu, (2015). Local Polynomial Chaos Expansion for Linear Differential Equations with High Dimensional Random Inputs SIAM Journal on Scientific Computing https://www.osti.gov/search/identifier:1225870 Document ID: 221453

John Davis Jakeman, Akil Narayan, Tao Zhou, (2015). The Christoffel Least Squares Algorithm For Collocation Approximations Unkn https://www.osti.gov/search/identifier:1347352 Document ID: 197329

Brian M. Adams, Lara E Bauman, William J. Bohnhoff, Keith Dalbey, John P. Eddy, Mohamed Salah Ebeida, Michael S. Eldred, Patricia D. Hough, Kenneth Hu, John Davis Jakeman, Laura Painton Swiler, John Adam Stephens, Dena Vigil, Timothy Michael Wildey, (2014). Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis Version 6.0 Users ManualVersion 6.0 Users Manual https://www.osti.gov/search/identifier:1177077 Document ID: 5336051

John Davis Jakeman, Michael S. Eldred, Khachik Sargsyan, (2014). Enhancing `1-minimization estimates of polynomial chaos expansions using basis selection Journal of Computational Physics https://www.osti.gov/search/identifier:1182997 Document ID: 143056

Habib N. Najm, Michael S. Eldred, Bert Debusschere, Kamaljit Singh Chowdhary, John Davis Jakeman, Cosmin Safta, Khachik Sargsyan, (2014). An Overview of Select UQ Algorithms and their Utility in Applications https://www.osti.gov/search/identifier:1494413 Document ID: 143239

Michael S. Eldred, Bert Debusschere, Kamaljit Singh Chowdhary, John Davis Jakeman, Habib N. Najm, Cosmin Safta, Khachik Sargsyan, (2014). Sandia Software Enabling Extreme-Scale Uncertainty Quantification https://www.osti.gov/search/identifier:1494264 Document ID: 143017

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