Center for Computing Research (CCR)

Center for Computing Research

John Davis Jakeman

John Davis Jakeman
Optimization & Uncertainty Quantification
Phone: 505/284-9097

Mailing address:
Sandia National Laboratories
P.O. Box 5800, MS 1318
Albuquerque, NM
  • Spectral stochastic expansions: I have extensive experience developing and applying Polynomial Chaos Expansions (PCE) for quantifying uncertainty. Recently I have combined iterative basis selection with compressed sensing to efficiently estimate PCE coefficients from structured and unstructured data. I have also developed locally and dimension adaptive sparse grid methods to construct high-dimensional surrogate models of expensive simulation models. Additional experience related to surrogate methods  includes Gaussian process emulation, discontinuity detection, gradient enhanced approximations and optimal experimental design using Leja sequences.
  • A posterori error analysis for finite elements and stochastic expansions: I have significant experience in numerical methods for Partial Differential Equations (PDE) including finite element and finite volume methods. I have contributed to the open source software project ANUGA which is a finite volume code that solves the shallow-water wave equations and is used to model tsunami and storm surges. I have also used finite elements in conjunction with polynomial chaos expansions, domain decomposition and dimension reduction to eficiently solve stochastic PDEs.
  • Optimization and inverse problems: I am interested in inferring random fields from (possibly limited) observational data. I have applied deterministic optimization and Bayesian inference to an ice-sheet model to develop initial conditions that can be used with various climate change scenarios to predict sea level rise due to ice-sheet melt. Important aspects of this problem include using Bayesian inference for extremely high-dimensional (> 100, 000) random field parameterizations and lower-dimensional random field modeling using Karhunen Loe´ve Expansions (KLE).
  • Deployment of software for uncertainty quantification: The complexity of methods used for uncertainty quantification ranges from those that are simple to code and use to those that are difficult to understand and apply. I am involved in a large team effort to build and deploy usable software tools for uncertainty quantification. The software is released in a C++ object oriented package called Dakota that aims to lessen the diffculty of applying UQ methods to real problems.  



  • 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-




Selected Publications & Presentations

  • Geraci, Gianluca, Michael S. Eldred, Alex Gorodetsky, John Davis Jakeman, "Recent advancements in Multilevel-Multifidelity techniques for forward UQ in the DARPA Sequoia project," Conference Paper, AIAA Scitech 2019 Forum, January 2019.
  • Gorodetsky, Alex, Gianluca Geraci, Michael S. Eldred, John Davis Jakeman, "A Generalized Framework for Approximate Control Variates," Journal Article, Journal of Computational Physics, Submitted February 2019.
  • Guillaume, Joseph, John Davis Jakeman, Stefano Marsili-Libelli, Michael Asher, Phillip Brunner, Barry Croke, Mary Hill, Anthony Jakeman, Karel Keesman, Saman Razavi, Johannes Stigter, "Introductory overview of identifiability analysis: A guide to evaluating whether you have the right type of data for your modeling purpose," Journal Article, Environmental Modelling & Software, Vol. 119, pp. 418–432, Accepted/Published September 2019.
  • Jakeman, John Davis, Michael S. Eldred, Gianluca Geraci, Alex Gorodetsky, "Adaptive multi-index collocation for uncertainty quantification and sensitivity analysis," Journal Article, International Journal for Numerical Methods in Engineering, Submitted January 2019.
  • Jakeman, John Davis, Fabian Franzelin, Michael S. Eldred, Akil Narayan, Dirk Pflueger, "Polynomial chaos expansions for dependent random variables," Journal Article, Computer Methods in Applied Mechanics and Engineering, Vol. 351, pp. 643–666, Accepted/Published July 2019.
  • Perego, Mauro, John Davis Jakeman, William Mark Severa, Lars Ruthotto, "Neural Networks Surrogates of PDE-based Dynamical Systems: Application to Ice Sheet Dynamics," Conference Paper, SIAM Conference on Computational Science and Engineering, Spokane, WA, February 2019.