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Uncertainty Quantification and High Performance Computing (Dagstuhl Seminar 16372)
High performance computing is a key technology to solve large-scale real-world simulation problems on parallel computers. Simulations for a fixed, deterministic set of parameters are current state of the art. However, there is a growing demand in methods to appropriately cope with uncertainties in those input parameters. This is addressed in the developing research field of uncertainty quantification. Here, Monte-Carlo methods are easy to parallelize and thus fit well for parallel computing. However, their weak approximation capabilities lead to inaccurate results. The Dagstuhl Seminar 16372 "Uncertainty Quantification and High Performance Computing" brought together experts in the fields of uncertainty quantification and high performance computing. Discussions on the latest numerical techniques beyond pure Monte-Carlo and with strong approximation capabilities were fostered. This has been put in context of real-world problems on parallel computers.
Uncertainty Quantification and High Performance Computing (Dagstuhl Seminar 16372)
High performance computing is a key technology to solve large-scale real-world simulation problems on parallel computers. Simulations for a fixed, deterministic set of parameters are current state of the art. However, there is a growing demand in methods to appropriately cope with uncertainties in those input parameters. This is addressed in the developing research field of uncertainty quantification. Here, Monte-Carlo methods are easy to parallelize and thus fit well for parallel computing. However, their weak approximation capabilities lead to inaccurate results. The Dagstuhl Seminar 16372 "Uncertainty Quantification and High Performance Computing" brought together experts in the fields of uncertainty quantification and high performance computing. Discussions on the latest numerical techniques beyond pure Monte-Carlo and with strong approximation capabilities were fostered. This has been put in context of real-world problems on parallel computers.
Uncertainty Quantification and High Performance Computing (Dagstuhl Seminar 16372)
Heuveline, Vincent (author) / Schick, Michael (author) / Webster, Clayton (author) / Zaspel, Peter (author) / Vincent Heuveline and Michael Schick and Clayton Webster and Peter Zaspel
2017-01-01
Dagstuhl Reports, Volume 6, Issue 9; ISSN 2192-5283
Conference paper
Electronic Resource
English
DDC:
690
Uncertainty Quantification and High Performance Computing (Dagstuhl Seminar 16372)
| BASE | 2017