Spatial Statistics and Uncertainty Quantification on Supercomputers

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Spatial Statistics and Uncertainty Quantification on Supercomputers

 19 - 21 Apr 2014

University of Bath
  • Finn Lindgren, University of Bath
  • Rob Scheichl, University of Bath
  • Tony Shardlow, University of Bath
  • Simon Wood, University of Bath

About:

Societal and environmental challenges have lead to a surge in the interest and development of novel statistical tools to deal with large data sets and of efficient uncertainty quantification (UQ) methods for complex engineering applications. There were two main aims for this meeting. The first aim was to bring together key researchers from statistics and from applied mathematics working in spatial statistics and in UQ. The second aim was to go beyond the discussion and analysis of algorithms applied to toy problems and to instead focus on real large scale applications and on the scalability of novel statistical and UQ tools on modern supercomputers.

Speakers:

  • Stephen Sain, Geophysical Statistics Project - Impact of Model Resolution for Regional Climate Experiments

  • Mike Christie, Heriot-Watt University - Multi-Objective Stochastic Sampling Algorithms for Uncertainty Quantification in Porous Media

  • Peter Challenor, University of Exeter - Combining Data and Models to Reconstruct the Climate of the Recent Past

  • Mark Girolami, University of Warwick - Differential Geometric Simulation Methods for Uncertainty Quantification in Large Scale PDE Systems

  • Kody Law, KAUST - Dimension-Independent, Likelihood-Informed MCMC Sampling Algorithms for Bayesian Inverse Problems

  • Geoff Nicholls, University of Oxford - Lateral Transfer of Traits on Phylogenetics

  • Annika Lang, Chalmers University - Gaussian Random Fields on the Sphere: One Class of Random Fields on Manifolds

  • Tim Sullivan, University of Warwick - Brittleness and Robustness of Bayesian Inference in Complex Systems

  • Gavin Shaddick, University of Bath - Challenges in the Integration of Air Pollution Estimates From Multiple Data Sources and Methods

  • Daniel Tartakovsky, UC - Uncertainty Quantification in Nonlinear Models of Multiphase Flow

  • Daniel Simpson, NTNU Trondheim - Barriers to Scalable Spatial Statistics

  • Roger Ghanem, U. Southern California - High Performance Algorithms for PDEs with Spatially Varying Stochastic Parameters

  • Aretha Teckentrup, Florida State University - Multilevel Markov Chain Monte Carlo Algorithms for Uncertainty Quantification

  • Paul Constantine, Colorado School of Mines - Active Subspace Methods for High-Dimensional Sensitivity Analysis

  • Omar Ghattas, ICES - Large-scale Bayesian Inversion with Application to Antarctic Ice Sheet Flow

  • Simon Cotter, University of Manchester - Parallel Adaptive Importance Sampling: Parallelism PAIS

  • Björn Gmeiner, University of Erlangen-Nuremberg - Massively Parallel Multi-level Monte-Carlo

Sponsors and Funders:

This workshop was sponsored by the Centre for Numerical Algorithms and Intelligent Software.