fig6

Predicting stacking fault energy in austenitic stainless steels via physical metallurgy-based machine learning approaches

Figure 6. Comparison of model predictions under different introduction methods. (A) Input features by RF models; (B) fully connected layer by CNN models; (C) output of the source model by Transfer learning. RF: Random forest; CNN: convolutional neural network.

Journal of Materials Informatics
ISSN 2770-372X (Online)
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