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**UPCOMING SEMINARS**

**1 December** at 11.00 in Lundmarksalen, coffee/tea from 10.30

**Bojana Rosić** (TU Braunschweig): Uncertainty quantification in
a Bayesian setting

Abstract:

Concrete and human bone tissue are typical examples of materials which exhibit
randomness in the mechanical response due to an uncertain heterogeneous
micro-structure. In order to develop an appropriate probabilistic macro-scale
mathematical description, the essential step is to address the material as well
as possible other sources of uncertainties (e.g. excitations, change in geometry
, etc.) in the model. By extending already existing deterministic models derived
from Helmholtz free energy and the dissipation functions characterising
ductile or quasi-brittle behaviour, the goal of this talk is to identify and
quantify
uncertainty in the system response. For this purpose a Bayesian probabilistic
setting is considered in which the modeller's a priori knowledge about the model
parameters and the available set of data obtained by experiments are taken into
account when identifying the corresponding probability distribution functions
of unknown parameters. Identification in the form of Bayesian inverse
problems -
in particular when experiments are performed repeatedly - requires an
effcient solution and representation of possibly high dimensional probabilistic
forward problems, i.e. the estimation of the measurement prediction given prior
assumption. An emergent idea is to propagate parameter uncertainties through
the model in a Galerkin manner in which the solution of the corresponding
differential
equations is represented by a set of stochastic basis polynomials, the
cardinality of which grows exponentially. To allow an effcient solution of
high-dimensional
problems this talk will present the new low-rank Galerkin schemes
combined with Bayesian machine learning approaches.

If you have suggestions of potential seminar speakers for the 2017-18 academic year please email us.

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This page was last modified on 30 November 2017.
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