Numerical Homogenization by Localized Orthogonal Decomposition

Häftad, Engelska, 2020

584 kr

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Beskrivning

This book presents the first survey of the Localized Orthogonal Decomposition (LOD) method, a pioneering approach for the numerical homogenization of partial differential equations with multiscale data beyond periodicity and scale separation. The authors provide a careful error analysis, including previously unpublished results, and a complete implementation of the method in MATLAB. They also reveal how the LOD method relates to classical homogenization and domain decomposition. Illustrated with numerical experiments that demonstrate the significance of the method, the book is enhanced by a survey of applications including eigenvalue problems and evolution problems.Numerical Homogenization by Localized Orthogonal Decomposition is appropriate for graduate students in applied mathematics, numerical analysis, and scientific computing. Researchers in the field of computational partial differential equations will find this self-contained book of interest, as will applied scientists and engineers interested in multiscale simulation.

Produktinformation

Utgivningsdatum:2020-12-30
Vikt:0 g
Format:Häftad
Språk:Engelska
Serie:SIAM Spotlights
Antal sidor:108
Förlag:Society for Industrial & Applied Mathematics,U.S.
ISBN:9781611976441

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Mer om författaren

Axel Målqvist is a professor of mathematics at Chalmers University of Technology and University of Gothenburg in Sweden. He is also a scientific adviser at the Fraunhofer Chalmers Centre. Prior to joining Gothenburg, he was an associate professor at Uppsala University, Sweden. Professor Målqvist's main scientific contributions are in the field of computational multiscale methods. In 2018, he received the Göran Gustafsson prize in mathematics.Daniel Peterseim holds the Chair for Computational Mathematics at the University of Augsburg, Germany. He was previously a full professor for numerical simulation at the University of Bonn. His research covers all aspects of computational partial differential equations with applications in engineering and physics. Professor Peterseim is most well known for his contributions in computational multiscale methods. In 2019, he received an ERC Consolidator Grant.