Prof. Tempone elected Program Director of the SIAM Uncertainty Quantification Activity Group (Jan 2013-Dec 2014) 1 min read · Fri, Feb 1 2013 News The SIAM Activity Group on Uncertainty Quantification (SIAG/UQ) fosters activity and collaboration on all aspects of the effects of uncertainty and error on mathematical descriptions of real phenomena. It seeks to promote the development of theory and methods to describe quantitatively the origin, propagation, and interplay of different sources of error and uncertainty in analysis and predictions of the behavior of complex systems, including biological, chemical, engineering, financial, geophysical, physical and social/political systems.
Prof. Tempone named Director of the new KAUST SRI Uncertainty Quantification Center 1 min read · Sun, Apr 29 2012 News The Office of Competitive Research Funds (OCRF) is delighted to formally announce the winners of the Strategic Research Initiative (SRI) competitive funding program, which was launched internationally in December 2010. Two winners were chosen from a 45-strong pool of applications from all over the world in a lengthy process involving rigorous peer review.
2012 MCSE Winter School: Applied Mathematics on Uncertainty Quantification 1 min read · Thu, Jan 5 2012 News Professor Tempone is in the committee of 2012 MCSE Winter School: Applied Mathematics on Uncertainty Quantification.
SIAM Review - A Stochastic Collocation Method for Elliptic Partial Differential Equations with Random Input Data 1 min read · Thu, May 6 2010 News uncertainty quantification Smolyak approximation This work proposes and analyzes a stochastic collocation method for solving elliptic partial differential equations with random coefficients and forcing terms. These input data are assumed to depend on a finite number of random variables.
SIAM J. Numer. Anal. - A Stochastic Collocation Method for Elliptic Partial Differential Equations with Random Input Data 1 min read · Sat, Jun 9 2007 News Stochastic PDEs uncertainty quantification In this paper, we propose and analyze a stochastic collocation method to solve elliptic partial differential equations with random coefficients and forcing terms (input data of the model). The input data are assumed to depend on a finite number of random variables.
