Towards the Optimal Design of Numerical Experiments - CEA - Commissariat à l’énergie atomique et aux énergies alternatives Access content directly
Journal Articles IEEE Transactions on Neural Networks Year : 2008

Towards the Optimal Design of Numerical Experiments

Abstract

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-optimal selection are investigated on the same benchmark problems.
Fichier principal
Vignette du fichier
200700000451.pdf (1.36 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

cea-02511993 , version 1 (19-03-2020)

Identifiers

Cite

Stéphane Gazut, Jean-Marc Martinez, Gerard Dreyfus, Yacine Oussar. Towards the Optimal Design of Numerical Experiments. IEEE Transactions on Neural Networks, 2008, 19 (5), pp.874-882. ⟨10.1109/TNN.2007.915111⟩. ⟨cea-02511993⟩
44 View
121 Download

Altmetric

Share

Gmail Facebook X LinkedIn More