University of Cambridge > Talks.cam > Isaac Newton Institute Seminar Series > Aspects of adaptive Galerkin FE for stochastic direct and inverse problems

Aspects of adaptive Galerkin FE for stochastic direct and inverse problems

Add to your list(s) Download to your calendar using vCal

If you have a question about this talk, please contact INI IT.

UNQW02 - Surrogate models for UQ in complex systems

Co-authors: Max Pfeffer (MPI MIS Leipzig), Manuel Marschall (WIAS Berlin), Reinhold Schneider (TU Berlin)

The Stochastic Galerkin Finite Element Method (SGFEM) is a common approach to numerically solve random PDEs with the aim to obtain a functional representation of the stochastic solution. As with any spectral method, the curse of dimensionality renders the approach challenging when the randomness depends on a large or countable infinite set of parameters. This makes function space adaptation and model reduction strategies a necessity. We review adaptive SGFEM based on reliable a posteriori error estimators for affine and non-affine parameter representations. Based on this, an adaptive explicit sampling-free Bayesian inversion in hierarchical tensor formats can be derived. As an outlook onto current research, a statistical learning viewpoint is presented, which connects concepts of UQ and machine learning from a Variational Monte Carlo perspective.

This talk is part of the Isaac Newton Institute Seminar Series series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.

 

© 2006-2024 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity