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Table 1 Best performing FFNN sorted by \(R^2(\mathrm {D}^\mathrm {T})\) after Phase 2

From: Application of artificial neural networks for the prediction of interface mechanics: a study on grain boundary constitutive behavior

IDArchitecturePhase 2  
\(\varvec{\{N,n,a(x)\}}\)\(\varvec{R^2(\mathrm {D}^\mathrm {T})}\)\(\varvec{R^2(\mathrm {D}^\mathrm {C})}\)\(\varvec{R^2(\mathrm {D}^\mathrm {V})}\)
FFNN1\(\{4,8,\tanh (x)\}\)0.94750.97560.9756
FFNN2\(\{4,7,\tanh (x)\}\)0.94420.97360.9736
FFNN3\(\{3,8,\tanh (x)\}\)0.94010.97270.9727