Fig. 3: LSTM architecture and calibration. | npj Computational Materials

Fig. 3: LSTM architecture and calibration.

From: Accelerating phase-field-based microstructure evolution predictions via surrogate models trained by machine learning methods

Fig. 3

a Learning curves as a function of the number of epochs for both training and validation sets. b Accuracy of the LSTM network for the absolute relative error, \({\mathrm {ARE}}^{(k)}\left({t}_{i}\right)\), as a function of the number of frames used for training. c Accuracy of the LSTM network for the normalized distance, \({D}^{(k)}\left({t}_{i}\right)\), as a function of the number of frames used for training. In b and c, the dashed green line indicates the 5% error value, while the black lines indicate the mean value of the absolute relative error and normalized distance respectively at various frames ti.

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