Date(s) - 12/06/2015
11:00 am - 12:30 pm
Institut Jean Le Rond d'Alembert
Categories No Categories
L’Institut Jean le Rond d’Alembert de l’UPMC aura le plaisir d’accueillir Tim Wildey de Sandia National Laboratory, USA pour un séminaire général exceptionnel, tour 55-65 salle 311.
Utilizing Adjoint-based Error Estimates to Adaptively Resolve Response Surface Approximations
Uncertainty and error are ubiquitous in predictive modeling and simulation due to unknown model parameters, boundary conditions and various sources of numerical error. Consequently, there is considerable interest in developing efficient and accurate methods to quantify the uncertainty in the outputs of a computational model. Monte Carlo techniques are the standard approach due to their relative ease of implementation and the fact that they effectively circumvent the curse of dimensionality. Unfortunately, the number of samples required to accurately estimate certain probabilistic quantities, especially the probability of high-risk, low-probability events, may be prohibitively large for high-fidelity computational models.
A number of recently developed methods for uncertainty quantification have focused on constructing response surface approximations of the input-to-output mapping using only a limited number of high-fidelity model evaluations. The fact that a very large number of samples can be efficiently evaluated using the response surface effectively reduces the statistical component of the error in the probabilistic quantity of interest. However, the deterministic component of the error may be quite large for each sample due to the standard sources of discretization error as well as the interpolation of the response surface approximation. The accumulation of these deterministic errors may significantly affect the accuracy of the probabilistic quantity of interest.
In this presentation, we show how adjoint-based techniques can be used to efficiently estimate the error in a quantity of interest computed from a sample of a response surface approximation. We then show howthese error estimates can be used to provide enhanced convergence, new adaptive strategies, and a means to avoid over-adapting the response surface beyond the accuracy of the spatial discretization. Finally, we demonstrate that these a posteriori estimates can also be used to guide adaptive improvement of a response surface approximation with the specific goal of accurately and efficiently estimating probabilities of events.
Event organized by Jérémy Foulon : email@example.com