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DTSTAMP:20230831T095745Z
LOCATION:Davos
DTSTART;TZID=Europe/Stockholm:20230626T112000
DTEND;TZID=Europe/Stockholm:20230626T115000
UID:submissions.pasc-conference.org_PASC23_sess104_pos125@linklings.com
SUMMARY:P26 - Global Sensitivity Analysis of High-Dimensional Models with 
 Correlated Inputs
DESCRIPTION:Poster\n\nJuraj Kardos and Olaf Schenk (Università della Svizz
 era italiana) and Derek Groen and Diana Suleimenova (Brunel University Lon
 don)\n\nGlobal sensitivity analysis is an important tool used in many doma
 ins of computational science to either gain insight into the mathematical 
 model and interaction of its parameters or study the uncertainty propagati
 on through the input-output interactions. This works introduces a comprehe
 nsive framework for conducting global sensitivity analysis on models with 
 correlated inputs. Traditional sensitivity analysis methods assume indepen
 dence between inputs and can provide misleading results when this assumpti
 on is violated. The proposed approach addresses parameter correlations usi
 ng transformations such as Rosenblatt and Cholesky, which are incorporated
  into a polynomial surrogate model used to evaluate sensitivity indices. T
 he effectiveness of the method is demonstrated through numerical experimen
 ts, which are conducted using the EasyVVUQ framework. The sensitivity anal
 ysis requires numerous execution of the target application, which requires
  significant computational resources. The numerical experiments are thus e
 xecuted using HPC platforms equipped with a metascheduler and workflow aut
 omation tools. The results of these experiments are discussed and provide 
 insights into the impact of correlated inputs on the sensitivity analysis.
 \n\nSession Chair: Elaine M. Raybourn (Sandia National Laboratories)
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