Article ID Journal Published Year Pages File Type
10644591 Computational Materials Science 2005 6 Pages PDF
Abstract
Using neural networks in a Bayesian framework, a model has been derived for the Ms temperature of steels over a wide range of compositions. By its design and by use of a more extensive database, this model improves over existing ones, by its accuracy and its ability to avoid wild predictions.
Related Topics
Physical Sciences and Engineering Engineering Computational Mechanics
Authors
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