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Título: | A model for predicting the Ms temperatures of steels. |
Autor: | Sourmail, Thomas; García Mateo, Carlos CSIC ORCID CVN | Palabras clave: | Martensite; Thermodynamics; Bayesian neural networks; Linear regression | Fecha de publicación: | 2005 | Editor: | Elsevier | Citación: | Computational Materials Science 34 (2005) 213–218 | Resumen: | 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. | Versión del editor: | http://dx.doi.org/10.1016/j.commatsci.2005.01.001 | URI: | http://hdl.handle.net/10261/3190 | DOI: | 10.1016/j.commatsci.2005.01.001 |
Aparece en las colecciones: | (CENIM) Artículos |
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New_Ms_model.pdf | 115,46 kB | Adobe PDF | Visualizar/Abrir |
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