Fig. 4: Performance and predictability of LSTM-trained surrogate model. | npj Computational Materials

Fig. 4: Performance and predictability of LSTM-trained surrogate model.

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

Fig. 4

a Predicted absolute relative error, \({\mathrm {ARE}}^{(k)}\left({t}_{i}\right)\), from frames t91 to t100. b Predicted normalized distance, \({D}^{(k)}\left({t}_{i}\right)\), from frames t91 to t100. In a, b 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. c Point-wise error comparison of the predicted vs. true autocorrelation for a microstructure randomly selected in our test set. d Cumulative probability distribution of the ARE at frame t100 for that microstructure. e Comparison of the predicted (dotted red line) vs. true (solid blue line) radial average autocorrelation.

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