Prediction from partial data, renormalization, and averaging

Alexandre J. Chorin; Ole H. Hald; Raz Kupferman

doi:10.1007/s10915-006-9089-5

Prediction from partial data, renormalization, and averaging

Alexandre J. Chorin^*, Ole H. Hald, Raz Kupferman

^*Corresponding author for this work

Einstein Institute of Mathematics

Research output: Contribution to journal › Article › peer-review

12 Scopus citations

Abstract

We summarize and compare our recent methods for reducing the complexity of computational problems, in particular dimensional reduction methods based on the Mori-Zwanzig formalism of statistical physics, block Monte-Carlo methods, and an averaging method for deriving an effective equation for a nonlinear wave propagation problem. We show that their common thread is scale change and renormalization.

Original language	English
Pages (from-to)	245-261
Number of pages	17
Journal	Journal of Scientific Computing
Volume	28
Issue number	2-3
DOIs	https://doi.org/10.1007/s10915-006-9089-5
State	Published - Sep 2006

Bibliographical note

Funding Information:
We are grateful to Prof. G.I. Barenblatt and to Dr. P. Stinis for many enlightening discussions, suggestions, and comments. This work was supported in part by the National Science Foundation under Grant DMS 97-32710, and in part by the Office of Science, Office of Advanced Scientific Computing Research, Mathematical, Information, and Computational Sciences Division, Applied Mathematical Sciences Subprogram, of the U.S. Department of Energy, under Contract No. DE-AC03-76SF00098.

Keywords

Averaging
Problem reduction
Renormalization
Scaling

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10.1007/s10915-006-9089-5

Cite this

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AU - Hald, Ole H.

AU - Kupferman, Raz

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KW - Scaling

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Prediction from partial data, renormalization, and averaging

Abstract

Bibliographical note

Keywords

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