Gilquin, Laurent Arnaud, Elise Prieur, Clémentine Janon, Alexandre

Among practitioners, the importance of inputs to a model output is commonly measured via the computation of Sobol' sensitivity indices. Various estimation strategies exist in the literature, most of them requiring a very high number of model evaluations. Designing methods that compete favorably both in terms of computational cost and accuracy is th...

Gilquin, Laurent Arnaud, Elise Prieur, Clémentine Janon, Alexandre

Among practitioners, the importance of inputs to a model output is commonly measured via the computation of Sobol' sensitivity indices. Various estimation strategies exist in the literature, most of them requiring a very high number of model evaluations. Designing methods that compete favorably both in terms of computational cost and accuracy is th...

Gilquin, Laurent Arnaud, Elise Prieur, Clémentine Janon, Alexandre

Among practitioners, the importance of inputs to a model output is commonly measured via the computation of Sobol' sensitivity indices. Various estimation strategies exist in the literature, most of them requiring a very high number of model evaluations. Designing methods that compete favorably both in terms of computational cost and accuracy is th...

Gilquin, Laurent Arnaud, Elise Prieur, Clémentine Janon, Alexandre

Among practitioners, the importance of inputs to a model output is commonly measured via the computation of Sobol' sensitivity indices. Various estimation strategies exist in the literature, most of them requiring a very high number of model evaluations. Designing methods that compete favorably both in terms of computational cost and accuracy is th...

Gilquin, Laurent Arnaud, Elise Prieur, Clémentine Janon, Alexandre

Among practitioners, the importance of inputs to a model output is commonly measured via the computation of Sobol' sensitivity indices. Various estimation strategies exist in the literature, most of them requiring a very high number of model evaluations. Designing methods that compete favorably both in terms of computational cost and accuracy is th...

Roustant, Olivier Padonou, Esperan Deville, Yves Clément, Aloïs Perrin, Guillaume Giorla, Jean Wynn, Henry

Gaussian processes (GP) are widely used as a metamodel for emulating time-consuming computer codes. We focus on problems involving categorical inputs, with a potentially large number L of levels (typically several tens), partitioned in G

Roustant, Olivier Padonou, Esperan Deville, Yves Clément, Aloïs Perrin, Guillaume Giorla, Jean Wynn, Henry

Gaussian processes (GP) are widely used as a metamodel for emulating time-consuming computer codes. We focus on problems involving categorical inputs, with a potentially large number L of levels (typically several tens), partitioned in G

Roustant, Olivier Padonou, Esperan Deville, Yves Clément, Aloïs Perrin, Guillaume Giorla, Jean Wynn, Henry

Gaussian processes (GP) are widely used as a metamodel for emulating time-consuming computer codes. We focus on problems involving categorical inputs, with a potentially large number L of levels (typically several tens), partitioned in G

Roustant, Olivier Padonou, Esperan Deville, Yves Clément, Aloïs Perrin, Guillaume Giorla, Jean Wynn, Henry

Gaussian processes (GP) are widely used as a metamodel for emulating time-consuming computer codes. We focus on problems involving categorical inputs, with a potentially large number L of levels (typically several tens), partitioned in G

Roustant, Olivier Padonou, Esperan Deville, Yves Clément, Aloïs Perrin, Guillaume Giorla, Jean Wynn, Henry

Gaussian processes (GP) are widely used as a metamodel for emulating time-consuming computer codes. We focus on problems involving categorical inputs, with a potentially large number L of levels (typically several tens), partitioned in G