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The core of project DESIGN is the investigation of statistical tools and methodologies for the design of optimal experiments in prediction and estimation problems. 

We study both non-parametric and non-linear parametric models. In the two cases, a central interest is on capturing the dependency of the optimal designs on the unknown distribution of the data, and on constructing efficient algorithms for their approximation. For instance, this resulted in the past in the definition of design criteria adapted to a particular model non-linearity, without resorting to simplifying normal asymptotic arguments. More recently, in the context of our participation to the GdR MASCOT-NUM, we focused on the design of experiments for random fields. Both model-free space filling designs — related to sphere packing and sphere covering problems — and designs for Gaussian Processes are studied. Applications of our results to computer experiments, as well as modelling of environmental phenomena, are on-going in the framework of various national and international collaborative projects. 

The project welcomes the application of highly motivated PhD and Post-doc candidates with a strong background in applied mathematics.