Methods of Applications for Experimental Design with Uncertainties

Quan Long, Serge Prudhomme, Raul Tempone

Experimental design is an important topic in engineering and sciences that pertains to model calibration, validation, and predictive modeling. As physiscal models are becoming increasingly more complex, the evaluation of corresponding utility values and optimization procedures is becoming computationally intensive. Techniques such as sparse quadratures, surrogate modeling, model reduction, and asymptotic approximations have been applied to the design of experiments. Research activities on the subject have recently gained momentum due to the current trend to carry out large-scale PDE related experimental design within a Bayesian framework. In this minisymposium, we propose to discuss the state-of-the-art as well as novel strategies, such as model reduction, sparse representation, asymptotic approximation, and multiscale approaches in experimental design. We also expect to have contributions from a wide range of applications from aerospace engineering, mechanical engineering, civil engineering, biomedical engineering, etc.