This is an announcement and an invitation to the

Workshop on Uncertainty Quantification 2018

The AGUQ of GAMM (Gesellschaft für Angewandte Mathematik und Mechanik) will hold its third stand-alone workshop on March 12.-14., 2018 at the Technical University Dortmund, Germany. Like its larger sister-organization SIAM’s SIAG UQ, the GAMM AGUQ coordinates UQ-related activities within GAMM. Its members come also from many non-German speaking countries in Europe. As with previous workshops, the scientific part of the meeting will consist of invited talks on recent developments in theory and application of uncertainty quantification methods as well as a poster session for which particularly PhD candidates and post-docs are encouraged to present their current research and work. In addition to the general part of the workshop there will be several featured topics. They cover on one hand recent trends within the field of uncertainty quantification, and on the other hand topics from neighbouring fields, with promising connections and applications in UQ. A particular aim of these featured topics is to bring people from different areas together and enable a cross-fertilization of ideas across different communities.

**ORGANIZERS**

- Oliver Ernst (Technische Universität Chemnitz)
- Hanno Gottschalk (Universität Wuppertal)
- Ivan Veselic (Technische Universität Dortmund)

**CONFIRMED Invited Speakers**

- Paul Dupuis
- Andreas Frommer
- Michael Günther
- Katja Ickstadt
- Jean-Christophe Mourrat
- Fabio Nobile
- Felix Otto
- Peter Stollmann

**PROGRAMME**

**
1. General UQ Session
**

Invited and contributed talks with poster session.

**Invited presentations: Paul Dupuis and Fabio Nobile**

**
2. Stochastic Modelling of Uncertainty
**

In UQ problems, one ultimately wants to analyse probability distributions of certain quantities of interest (QoI). In models used to describe the problem, randomness is often introduced in a somewhat ad-hoc fashion. This leaves much room for variation and raises various questions from the modelling perspective which we will discuss in this section, e.g.: How does the choice of model effect the probability law of the QoI? What are the computational advantages/disadvantages of different stochastic models? Are there effective models which can be rigorously justified by ab initio deductions from laws of physics? Can the modelling experience from neighbouring scientific fields such as statistical physics or geostatistics provide promising insights for the improvement of currently used approaches in UQ?

**
Invited presentations: Katja Ickstadt and Peter Stollmann
**

**
3. Stochastic Homogenization and Analysis of Fluctuations
**

Homogenization is a method of studying partial differential equations with rapidly oscillating coefficients where one approximates the original problem by an effective model obtained from asymptotic analysis. This model is often called 'homogenization formula' and is achieved by sending the length scale characteristic of the oscillations to zero. If the variation of the coefficients on small scales is random, a stochastic variant of homogenization theory can be applied. Here again one can obtain formulas for homogenized coefficients using an averaging procedure. More detailed questions concern fluctuations around the deterministic limit which is achieved through averaging. The distribution of the fluctuations encode uncertainty in a similar way as is the case for traditional UQ problems.

**
Invited presentations: Jean-Christophe Mourrat and Felix Otto
**

**
4. UQ Meets Lattice QFT
**

An integral part of solving many UQ problems is the simulation of infinite or high dimensional stochastic systems. Methodically, one encounters the same challenge when studying lattice quantum field theory via path integrals. We wish to facilitate exchange between experts in these two fields in order to compare the current state of knowledge, the best algorithms available and challenges encountered.

**
Invited presentations: Andreas Frommer and Michael Günther
**

**
A. Hands-on Tutorial for Covariance and Semivariogram Estimation with R
**

Many methods of UQ rely on (exponentiated) Gaussian processes, e.g. as a model for conductivity in an inhomogeneous porous medium. The crucial input to these methods is the covariance structure of the Gaussian process. In this hands-on tutorial based on the statistical computing environment R, the geostatistical estimation of covariances and semivariograms is demonstrated and relevant R-extensions (packages) are introduced. We also demonstrate how to estimate the uncertainty that originates from the statistical error in estimation. The tutorial is limited to 12 participants.

**
B. GAMM AGUQ Business Meeting
**

Open for all members of the GAMM Activity Group Uncertainty Quantification (www.tu-chemnitz.de/gamm-uq) and those interested in participating.

Details on the online registration procedure will be provided in a forthcoming announcement.

TU Dortmund

Fakultät für Mathematik

Lehrstuhl IX

Vogelpothsweg 87

44227 Dortmund

You find us on the 6th floor of the Math tower .

Secretary: Mrs Janine Textor

Room M 620

E-mail:

janine.textor@tu-dortmund.de

Tel. (0231) 755-3063

Fax (0231) 755-5219