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.

[1]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[2]  David J. C. MacKay,et al.  BAYESIAN NON-LINEAR MODELING FOR THE PREDICTION COMPETITION , 1996 .

[3]  Geoffrey E. Hinton,et al.  Bayesian Learning for Neural Networks , 1995 .

[4]  D. Mackay,et al.  Bayesian Neural Network Analysis of Fatigue Crack Growth Rate in Nickel Base Superalloys , 1996 .

[5]  Arun D Kulkarni,et al.  Neural Networks for Pattern Recognition , 1991 .

[6]  J. Hadamard,et al.  Lectures on Cauchy's Problem in Linear Partial Differential Equations , 1924 .

[7]  Martin Brown,et al.  SUPANOVA: a sparse, transparent modelling approach , 1999, Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468).

[8]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.