Crombecq, Karel Gorissen, Dirk Deschrijver, Dirk Dhaene, Tom

Many complex real-world systems can be accurately modeled by simulations. However, high-fidelity simulations may take hours or even days to compute. Because this can be impractical, a surrogate model is often used to approximate the dynamic behavior of the original simulator. This model can then be used as a cheap, drop-in replacement for the simul...

Deschrijver, Dirk Crombecq, Karel Nguyen, Huu Minh Dhaene, Tom

This paper presents a new adaptive sampling strategy for the parametric macromodeling of S-parameter-based frequency responses. It can be linked directly with the simulator to determine up front a sparse set of data samples that characterize the design space. This approach limits the overall simulation and macromodeling time. The resulting sample d...

Vazquez, Emmanuel Bect, Julien

This paper deals with the convergence of the expected improvement algorithm, a popular global optimization algorithm based on a Gaussian process model of the function to be optimized. The first result is that under some mild hypotheses on the covariance function k of the Gaussian process, the expected improvement algorithm produces a dense sequence...

Pronzato, Luc
Published in
Metrika

We study the consistency of parameter estimators in adaptive designs generated by a one-step ahead D-optimal algorithm. We show that when the design space is finite, under mild conditions the least-squares estimator in a nonlinear regression model is strongly consistent and the information matrix evaluated at the current estimated value of the para...

Crombecq, Karel Dhaene, Tom

In this paper, the authors compare a Monte Carlo method and an optimization-based approach using genetic algorithms for sequentially generating space-filling experimental designs. It is shown that Monte Carlo methods perform better than genetic algorithms for this specific problem.

Pronzato, Luc

Optimal design under a cost constraint is considered, with a scalar coefficient setting the compromise between information (i.e., precision of the estimation of the model parameters) and cost. For suitable cost functions, by increasing the value of the coefficient one can force the support points of an optimal design measure to concentrate around p...

Pronzato, Luc

We present new conditions for the strong consistency and asymptotic normality of the least squares estimator in nonlinear stochastic models when the design variables vary in a finite set. The application to self-tuning optimisation is considered, with a simple adaptive strategy that guarantees simultaneously the convergence to the optimum and the s...

Bettinger, Régis Duchêne, Pascal Pronzato, Luc

A design method presented in a previous paper for the sequential generation of observation sites used for the inversion of a prediction model is extended to cope with practical issues such as delayed observations and design of batches of imposed size. The final objective of the construction is to be able to associate with any target T in the output...

Pronzato, Luc

Under the condition that the design space is finite, new sufficient conditions for the strong consistency and asymptotic normality of the least-squares estimator in nonlinear stochastic regression models are derived. Similar conditions are obtained for the maximum-likelihood estimator in Bernoulli type experiments. Consequences on the sequential de...

Pronzato, Luc

We consider a parameter estimation problem with independent observations where one samples from a finite population of independent and identically distributed experimental conditions X. The size of the population is N but only n samples, a proportion alpha of N, can be used. The quality of a sample is measured by a regular optimality criterion phi(...