Global sensitivity analysis using sparse high dimensional model representations generated by the group method of data handling

@article{Lambert2016GlobalSA,
  title={Global sensitivity analysis using sparse high dimensional model representations generated by the group method of data handling},
  author={Romain S. C. Lambert and Frank Lemke and Sergei S. Kucherenko and Shufang Song and Nilay Shah},
  journal={Mathematics and Computers in Simulation},
  year={2016},
  volume={128},
  pages={42-54}
}
In this paper, the parameter selection capabilities of the group method of data handling (GMDH) as an inductive self-organizing modelling method are used to construct sparse random sampling high dimensional model representations (RS-HDMR), from which the Sobol’s first and second order global sensitivity indices can be derived. The proposed method is capable of dealing with high-dimensional problems without the prior use of a screening technique and can perform with a relatively limited number… CONTINUE READING

Citations

Publications citing this paper.

References

Publications referenced by this paper.
SHOWING 1-10 OF 34 REFERENCES

Practical approaches to construct RS-HDMR component functions

  • G. Li, S.-W. Wang, H. Rabitz
  • J. Phys. Chem. A 106 (37)
  • 2002
Highly Influential
11 Excerpts

Stochastic free vibration analysis of angle-ply composite plates—a RS-HDMR approach

  • S. Dey, T. Mukhopadhyay, S. Adhikari
  • Compos. Struct. 122
  • 2015

in: G

  • B. Ioos, P. Lemaitre
  • Dellino, C. Moloni (Eds.), Uncertainty Management…
  • 2015
1 Excerpt

Similar Papers

Loading similar papers…