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Self-Adaptive Numerical Methods for Computationally Challenging Problems

dc.date.accessioned2019-10-24T15:21:58Z
dc.date.available2019-10-24T15:21:58Z
dc.date.issued2016
dc.identifier.urihttp://publications.mfo.de/handle/mfo/3546
dc.description.abstractSelf-adaptive numerical methods provide a powerful and automatic approach in scientific computing. In particular, Adaptive Mesh Refinement (AMR) algorithms have been widely used in computational science and engineering and have become a necessary tool in computer simulations of complex natural and engineering problems. The key ingredient for success of self-adaptive numerical methods is a posteriori error estimates that are able to accurately locate sources of global and local error in the current approximation. The workshop creates a forum for junior and senior researchers in numerical analysis and computational science and engineering to discuss recent advances, initiates future research projects, and establishes new collaborations on convergence theory of adaptive numerical methods and on the construction and analysis of efficient, reliable, and robust a posteriori error estimators for computationally challenging problems.
dc.titleSelf-Adaptive Numerical Methods for Computationally Challenging Problems
dc.identifier.doi10.14760/OWR-2016-42
local.series.idOWR-2016-42
local.subject.msc76
local.subject.msc73
local.subject.msc65
local.sortindex986
local.date-range04 Sep - 10 Sep 2016
local.workshopcode1636
local.workshoptitleSelf-Adaptive Numerical Methods for Computationally Challenging Problems
local.organizersRandy Bank, La Jolla; Zhiqiang Cai, West Lafayette; Rüdiger Verfürth, Bochum
local.report-nameWorkshop Report 2016,42
local.opc-photo-id1636
local.publishers-doi10.4171/OWR/2016/42
local.ems-referenceBank Randolph, Cai Zhiqiang, Verfürth Rüdiger: Self-Adaptive Numerical Methods for Computationally Challenging Problems. Oberwolfach Rep. 13 (2016), 2399-2464. doi: 10.4171/OWR/2016/42


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