Statistics Seminar

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Speaker: Richard K. Archibald, staff scientist, Computational Mathematics at ORNL

Title: High performance computer algorithms for function approximation and error estimation on arbitrary sparse samples


Stochastic collocation methods are an attractive choice to characterize uncertainty because of their non-intrusive nature. High dimensional stochastic spaces can be approximated well for smooth functions with sparse grids. There has been a focus in extending this approach to non-smooth functions using adaptive sparse grids. We have developed a fast method that can capture piecewise smooth functions in high dimensions with high order and low computational cost. This method can be used for both approximation and error estimation of stochastic simulations where the computations can either be guided or come from a legacy database.

Richard K. Archibald is a staff scientist in the Computational Mathematics group at ORNL. He held the Alston S. Householder Fellowship in Scientific Computing from 2005 until his staff appointment in 2007. Archibald received both his BS in physics and his MS in mathematics from the University of Alberta in Edmonton, Canada in 1996 and 1998, respectively. He obtained his PhD in mathematics from the University of Alberta-Edmonton in 2002.

Archibald¹s current research interests include developing uncertainty quantification algorithms for climate models, designing algorithms for the next generation of high-performance architecture, and establishing long-time integration steps for the dynamical core of climate models at high resolution. He is presently involved in the Center for Advanced Architecture¹s HOMME project, as well as ³Ultra-High Resolution Global Climate Simulations² with principal investigator Jim Hack and researcher Kate Evans. This project seeks to develop the scientific framework to ascertain the benefit of employing very-high-resolution global models to investigate regional-scale phenomena.


  • Workflow Status: Published
  • Created By: Anita Race
  • Created: 09/05/2012
  • Modified By: Fletcher Moore
  • Modified: 10/07/2016


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