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Noise in Algorithm Refinement Methods
Found in: Computing in Science and Engineering
By Francis J. Alexander, Daniel M. Tartakovsky, Alejandro L. Garcia
Issue Date:May 2005
pp. 32-38
Hybrid or algorithm refinement (AR) schemes have focused mainly on the mean behavior of system states. However, variances in these behaviors, such as spontaneous fluctuations, are important for modeling certain phenomena. This article discusses the effects...
 
Big Data
Found in: Computing in Science and Engineering
By Francis J. Alexander,Adolfy Hoisie,Alexander Szalay
Issue Date:November 2011
pp. 10-13
This introduction to the special issue on big data discusses the significant scientific opportunities offered by massive amounts of data, along with some directions for future research.
 
Adaptive Mesh Refinement for Multiscale Nonequilibrium Physics
Found in: Computing in Science and Engineering
By Daniel F. Martin, Phillip Colella, Marian Anghel, Francis J. Alexander
Issue Date:May 2005
pp. 24-31
In this article, the authors demonstrate how to use adaptive mesh refinement (AMR) methods for the study of phase transition kinetics. In particular, they apply a block-structured AMR to investigate phase ordering in the time-dependent Ginzburg-Landau equa...
 
Machine Learning [Guest editor's introduction]
Found in: Computing in Science & Engineering
By Francis J. Alexander
Issue Date:September 2013
pp. 9-11
The guest editor discusses some recent advances in machine learning and their applications to exciting new problem areas.
 
Guest Editors' Introduction: Multiphysics Modeling
Found in: Computing in Science and Engineering
By Daniel M. Tartakovsky, Francis J. Alexander
Issue Date:May 2005
pp. 14-15
Fueled by breakthroughs in both hardware and algorithm development, the past few decades have witnessed an explosive growth in computational power, which has led to remarkable advances in various fields of science and technology, such as the mapping of the...
 
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