Accurate and online-efficient evaluation of the a posteriori error bound in the reduced basis method
Résumé
The reduced basis method is a model reduction technique yielding substantial savings of computational time when a solution to a parametrized equation has to be computed for many values of the parameter. Certification of the approximation is possible by means of an a posteriori error estimator, delivering an upper bound on the error. Under appropriate assumptions, this error bound is computed with an algorithm of complexity independent of the size of the full problem. In practice, the evaluation of the error estimator can become very sensitive to round-off errors. We propose herein an explanation of this fact. A first remedy has been proposed in [F. Casenave, C. R. Acad. Sci.]. Herein, we improve this remedy by proposing a new approximation of the error estimator using the Empirical Interpolation Method. This method achieves higher levels of accuracy and requires potentially less precomputations than the usual formula. A stabilized version is also derived. The method is illustrated on a simple one-dimensional diffusion problem and a three-dimensional acoustic scattering problem solved by a boundary element method.