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Domain Decomposition Methods in Science and Engineering XXV de Ronald Haynes

Domain Decomposition Methods in Science and Engineering XXV: Lecture Notes in Computational Science and Engineering, cartea 138

Editat de Ronald Haynes, Scott MacLachlan, Xiao-Chuan Cai, Laurence Halpern, Hyea Hyun Kim, Axel Klawonn, Olof Widlund
en Limba Engleză Paperback – 26 oct 2021
These are the proceedings of the 25th International Conference on Domain Decomposition Methods in Science and Engineering, which was held in St. John's, Newfoundland, Canada in July 2018. 
Domain decomposition methods are iterative methods for solving the often very large systems of equations that arise when engineering problems are discretized, frequently using finite elements or other modern techniques. These methods are specifically designed to make effective use of massively parallel, high-performance computing systems.
The book presents both theoretical and computational advances in this domain, reflecting the state of art in 2018.

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Specificații

ISBN-13: 9783030567521
ISBN-10: 3030567524
Pagini: 536
Ilustrații: XXVI, 508 p.
Dimensiuni: 155 x 235 x 29 mm
Greutate: 0.8 kg
Ediția:1st ed. 2020
Editura: Springer
Colecția Lecture Notes in Computational Science and Engineering
Seria Lecture Notes in Computational Science and Engineering

Locul publicării:Cham, Switzerland

Cuprins

Part I Plenary Talks (PT).- Part II Talks in Minisymposia (MT).- Part III Contributed Talks and Posters (CT).

Textul de pe ultima copertă

These are the proceedings of the 25th International Conference on Domain Decomposition Methods in Science and Engineering, which was held in St. John's, Newfoundland, Canada in July 2018. 
Domain decomposition methods are iterative methods for solving the often very large systems of equations that arise when engineering problems are discretized, frequently using finite elements or other modern techniques. These methods are specifically designed to make effective use of massively parallel, high-performance computing systems.
The book presents both theoretical and computational advances in this domain, reflecting the state of art in 2018.