Goals of the chair

Over the last few decades, uncertainty quantification has proved highly effective for analyzing physical systems, in fields such as aeronautics, space and energy.

More recently, it has also been demonstrated that uncertainty quantification methods can be advantageously transferred from physical applications to artificial intelligence (AI) systems, in particular for the analysis of explainability and fairness.

Principal investigator

The UQPhysAI Chair will improve fundamental understanding and develop robust and widely applicable algorithms for two cornerstones of uncertainty quantification: sensitivity analysis and active learning with Bayesian models.

For sensitivity analysis, the focus will be on causal analysis and the large size and complexity of inputs and outputs.

With regard to Bayesian models and active learning, the Chair will focus on nested and coupled environments, and on exploiting new and more diverse techniques for quantifying uncertainties.

Methodological developments will be fed by and applied to realistic problems and data for physical and artificial intelligence systems, in collaboration with industrial partners: ONERA, Airbus, Liebherr and Vitesco.

Co-chair

Participants

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