Fig. 1
From: Higher-order factorization machine for accurate surrogate modeling in material design
Active learning employing the 2nd and 3rd-order FMs. (a) Preparing a training dataset consisting of input binary vectors (\({\bf x}_{i}\)) and associated outputs \({\text{FOM}}_{i}\). (b) Formulating a surrogate function with the training dataset. (c) Evaluating the surrogate function to identify the optimal binary vector. (d) Calculating FOM for the optimal binary vector. (e) Updating the training dataset with the identified binary vector and the corresponding FOM.
