Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10644591 | Computational Materials Science | 2005 | 6 Pages |
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
T. Sourmail, C. Garcia-Mateo,