Large-Scale Scientific Computing 10th International Conference, LSSC 2015, Sozopol, Bulgaria, June 8-12, 2015. Revised Selected Papers için kapak resmi
Large-Scale Scientific Computing 10th International Conference, LSSC 2015, Sozopol, Bulgaria, June 8-12, 2015. Revised Selected Papers
Başlık:
Large-Scale Scientific Computing 10th International Conference, LSSC 2015, Sozopol, Bulgaria, June 8-12, 2015. Revised Selected Papers
Yazar:
Lirkov, Ivan. editor.
ISBN:
9783319265209
Edisyon:
1st ed. 2015.
Fiziksel Niteleme:
XIII, 444 p. 127 illus. in color. online resource.
Seri:
Lecture Notes in Computer Science, 9374
İçindekiler:
Multilevel Methods on Graphs -- Mathematical Modeling and Analysis of PDEs Describing Physical Problems -- Numerical Methods for Multiphysics Problems -- Control and Uncertain Systems -- Enabling Exascale Computation -- Efficient Algorithms for Hybrid HPC Systems -- Applications of Metaheuristics to Large-Scale Problems -- Computational Microelectronics — from Monte Carlo to Deterministic Approaches -- Large-Scale Models: Numerical Methods, Paralell Computations and Applications. .
Özet:
This book constitutes the thoroughly refereed post-conference proceedings of the 10th International Conference on Large-Scale Scientific Computations, LSSC 2015, held in Sozopol, Bulgaria, in June 2015. The 49 revised full papers presented were carefully reviewed and selected from 64 submissions. The general theme for LSSC 2015 was Large-Scale Scientific Computing with a particular focus on the organized special sessions: enabling exascale computation; control and uncertain systems; computational microelectronics - from monte carlo to deterministic approaches; numerical methods for multiphysics problems; large-scale models: numerical methods, parallel computations and applications; mathematical modeling and analysis of PDEs describing physical problems; a posteriori error control and iterative methods for maxwell type problems; efficient algorithms for hybrid HPC systems; multilevel methods on graphs; and applications of metaheuristics to large-scale problems.