Towards the optimal design of numerical experiments.
This paper addresses the problem of the optimal design of numerical experiments for the construction of nonlinear surrogate models. We describe a new method, called learner disagreement from experiment resampling (LDR), which borrows ideas from active learning and from resampling methods: the analysis of the divergence of the predictions provided by a population of models, constructed by resampling, allows an iterative determination of the point of input space, where a numerical experiment should be performed in order to improve the accuracy of the predictor. The LDR method is illustrated on neural network models with bootstrap resampling, and on orthogonal polynomials with leave-one-out resampling. Other methods of experimental design such as random selection and D-optimal selection are investigated on the same benchmark problems.
Gazut
Stéfane
S
DM2S/SFME Centre d'Etudes de Saclay, 91191 Gif sur Yvette, France. stephane.gazut@cea.fr
Martinez
Jean-Marc
JM
Dreyfus
Gérard
G
Oussar
Yacine
Y
D000465:Algorithms; D001185:Artificial Intelligence; D015233:Models, Statistical; D009010:Monte Carlo Method; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D014965:X-Rays