Multiscale Scientific Computation: Review 2001

  • Achi Brandt
Part of the Lecture Notes in Computational Science and Engineering book series (LNCSE, volume 20)


Most of the fundamental problems in physics, chemistry and engineering involve computation too hard even for future supercomputers, if conventional mathematical approaches are used. The reason is always a product of several complexity factors associated with the wide range of space and time scales characteristic to such problems. Each of these complexity factors can in principle be removed by various multiscale algorithms, i.e., employing separate processing at each scale of the problem, combined with interscale iterative interactions. A wide range of multiscale computational methods is described, emphasizing main ideas and inter-relations between various fields. The reported areas include: top-efficiency multigrid methods in fluid dynamics; inverse PDE problems and data assimilation; feedback optimal control; PDE solvers on unbounded domains and on adaptable grids; wave/ray methods for highly indefinite equations; rigorous quantitative analysis of multigrid; manyeigenfunction problems and ab-initio quantum chemistry; fast evaluation of integral transforms on adaptive grids; multigrid Dirac solvers; fast inverse-matrix and determinant calculations and updates; multiscale Monte-Carlo methods in statistical physics, including the renormalization multigrid (RMG) methods; molecular mechanics (including fast force summation, fast macromolecular energy minimization, and Monte-Carlo methods at equilibrium, both for macromolecules and for large ensembles of small molecules); combination of small-scale equilibrium with large-scale dynamics; image processing (edge detection and picture segmentation); tomography (medical imaging and radar reconstruction); efficient, general and highly accurate algebraic multigrid (AMG) and homogenization schemes; fast practical graph algorithms; data clustering; and multiscale approaches to global optimization.


Coarse Grid Gauge Field Fine Level Multigrid Method Coarse Level 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Achi Brandt
    • 1
  1. 1.The Weizmann Institute of ScienceRehovotIsrael

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