Bayesian Inference for Uncertainty Quantification and Inverse Problems (Talk)
The predictive simulation of engineering systems increasingly rests on the synthesis of physical models and experimental data. In this context, Bayesian inference establishes a framework for quantifying the encountered uncertainties and fusing the available information. A summary and discussion of some recently emerged methods for uncertainty propagation (polynomial chaos expansions) and related MCMC-free techniques for posterior computation (spectral likelihood expansions, optimal transportation theory) is presented.
Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems