Data driven knowledge extraction of materials properties
The problem of modelling a large commercial materials dataset using advanced adaptive numeric methods is described. The various approaches are outlined, emphasising their characteristics with respect to generalisation, performance and transparency. A highly novel support vector machine (SVM) approach is taken incorporating a high degree of transparency via a full analysis of variance (ANOVA) expansion. Using an example which predicts 0.2% proof stress from a set of materials features, different modelling techniques are compared by benchmarking against independent test data.