Identifying intrinsic variability in multivariate systems through linearized inverse methods

G. CeleuxINRIA Saclay, Université Paris-Sud, Batiment 425, F91405 Orsay Cedex, France

A. GrimaudINRIA Saclay, Institut de Mathématiques de Luminy Case 907, F13288 Marseille Cedex 9, France

Y. LefèbvreEDF R&D, 6 quai Watier, F78401 Chatou Cedex, France

E. de RocquignyEDF R&D, 6 quai Watier, F78401 Chatou Cedex, France

Figures & data

Table 1. Parameters estimates for the flooding model via EM, from 25 random initial positions, with fixed m_{K_s} = 30 and σ_{K_s} = 7.5.

Display Table

Table 2. Parameters estimates for the flooding model via ECME, from 25 random initial positions, with fixed m_{K_s} = 30 and σ_{K_s} = 7.5.

Display Table

Table 3. Estimates, NEC and Sensibility indices (SI) for the mean vector with the ECME algorithm, for the thermo-hydraulics model.

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Figure 1. The values of the gradient of the H function for the variables six in function of the variable ten.

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Identifying intrinsic variability in multivariate systems through linearized inverse methods

Table 1. Parameters estimates for the flooding model via EM, from 25 random initial positions, with fixed m_{K_s} = 30 and σ_{K_s} = 7.5.

Table 2. Parameters estimates for the flooding model via ECME, from 25 random initial positions, with fixed m_{K_s} = 30 and σ_{K_s} = 7.5.

Table 3. Estimates, NEC and Sensibility indices (SI) for the mean vector with the ECME algorithm, for the thermo-hydraulics model.

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Identifying intrinsic variability in multivariate systems through linearized inverse methods

Figures & data

Table 1. Parameters estimates for the flooding model via EM, from 25 random initial positions, with fixed mKs = 30 and σKs = 7.5.

Table 2. Parameters estimates for the flooding model via ECME, from 25 random initial positions, with fixed mKs = 30 and σKs = 7.5.

Table 3. Estimates, NEC and Sensibility indices (SI) for the mean vector with the ECME algorithm, for the thermo-hydraulics model.

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Table 1. Parameters estimates for the flooding model via EM, from 25 random initial positions, with fixed m_{K_s} = 30 and σ_{K_s} = 7.5.

Table 2. Parameters estimates for the flooding model via ECME, from 25 random initial positions, with fixed m_{K_s} = 30 and σ_{K_s} = 7.5.