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Published online: 2017-04-22
Self-Adaptive Numerical Methods for Computationally Challenging ProblemsRandolph E. Bank, Zhiqiang Cai and Rüdiger Verfürth (1) University of California, San Diego, USA
(2) Purdue University, West Lafayette, USA
(3) Ruhr-Universität Bochum, Germany
Self-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.
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Bank 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