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Abstract

Neural networks have pervaded all aspects of materials science resulting in the discovery of new phenomena and have been used in quantitative design and control. At the same time, they have introduced a culture in which both noise and modeling uncertainties are considered in order to realize the value of empirical modeling. This review deals with all of these aspects using concrete examples to highlight the progress made, whilst at the same time emphasizing the limitations of the method. Copyright © 2009 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 1: 000-000, 2009