Model and dimension reduction in uncertain and dynamic systems

The organizers of the first spring 2020 workshop, Mathematics of Reduced Order Models that took place Feb 17-21. From left to right on the picture, Olga Mula, Albert Cohen, Peter Benner, Akil Narayan, Karen Veroy-Grepl, and Serkan Gugercin.

The pyMOR development team is happy to announce the release of version 2019.2 of pyMOR

The highlights of the new pyMOR version are:

– Improved model and reductor design makes pyMOR easier to extend.
– Extended VectorArray interface with generic complex number support.
– Improved and extended system-theoretic MOR methods.
– Builtin support for model outputs and parameter sensitivities.

The full release notes can be found here.

pyMOR is a software library for building model order reduction
applications with the Python programming language. Implemented
algorithms include reduced basis methods for parametric linear and
non-linear problems, as well as system-theoretic methods such as
balanced truncation or IRKA. All algorithms in pyMOR are formulated in
terms of abstract interfaces for seamless integration with external
PDE solver packages. Moreover, pure Python implementations of finite
element and finite volume discretizations using the NumPy/SciPy
scientific computing stack are provided for getting started quickly.

Matrix manifolds and applications. PhD project in applied mathematics

Description. The Department of Mathematics and Computer Science (IMADA) at the University of Southern Denmark (SDU) invites applications for a PhD position(f/m) in the area of applied mathematics.

We seek an ambitious candidate with a strong background in one or more of the following areas:
• Numerical linear algebra
• Numerical analysis (in particular: matrix analysis)
• Model reduction
• Differential geometry

Requirements. The successful applicant holds a master degree in pure or applied mathematics. Applications should include a CV, the subject of the master thesis, a list of scientific skills and contact information for two references.

The employment is for three years at a competitive salary. The proposed starting date is September 2018, but earlier and later starting dates can be negotiated.

Details and access to the online application form:

Interested candidates are invited to contact
Ralf Zimmermann

11 PhD positions in an European Industrial Doctorate, extended deadline December 15, 2017

Extended deadline: December 15, 2017

ROMSOC is a European Industrial Doctorate (EID) project in the programme Innovative Training Networks (ITN) and part of Marie Sklodowska Curie Actions within the Horizon 2020 programme. The ROMSOC EID Network brings together 15 international academic institutions and 11 industry partners and supports the recruitment of eleven Early Stage Researchers (ESRs). Each ESR will be working on an individual research project in the host institution with secondments related to their research in other academic and industrial partners of the network. The research is focused on three major topics: coupling methods, model reduction methods, and optimization methods, for industrial applications in well selected areas, such as optical and electronic systems, economic processes, and materials. The ROMSOC EID Network offers a unique research environment, where leading academics and innovative industries will integrate ESRs into their research teams for the training period, providing an excellent structured training programme in modelling, simulation and optimization of whole products and processes.

We seek excellent open-minded and team-spirited PhD candidates who will get unique international, interdisciplinary and inter-sectoral training in scientific and transferable skills by distinguished leaders from academia and industry. Deadline applications: 25 November. The calls for the ESR positions in the ROMSOC project have now been published at the EURAXESS webpage:

11 Early Stage Research Positions available in MSCA-ITN-EID project ROMSOC
(EURAXESS Job Offer id: 257318)
The following positions are available:

RTC implementation of high-performance algorithms for adaptive optics control
Reference number: ROMSOC-ESR01
Johannes-Kepler Universität, Linz, Austria

Mathematical modelling and numerical simulation of coupled thermo-acoustic multi-layer systems for enabling particle velocity measurements in the presence of airflow.
Reference number: ROMSOC-ESR02
ITMATI, Santiago de Compostela, Spain

FreeForm Optics applications of Optimal Transport Solvers
Reference number: ROMSOC-ESR03
INRIA, Paris, France

Data driven model adaptations of coil sensitivities in MR systems.
Reference number: ROMSOC-ESR04
University of Bremen, Bremen, Germany

Coupling of Model Order Reduction and Multirate Techniques for coupled heterogeneous time-dependent systems in an industrial optimization flow.
Reference number: ROMSOC-ESR05
Bergische Universität Wuppertal, Wuppertal, Germany

Model order reduction for parametric high dimensional models in the analysis of financial risk.
Reference number: ROMSOC-ESR06
Technische Universität Berlin, Berlin, Germany and MathConsult GmbH, Linz, Austria

Integrated Optimization of International Transportation Networks.
Reference number: ROMSOC-ESR07
Friedrich-Alexaner Universität Erlangen-Nürnberg, Erlangen, Germany

Efficient computational strategies for complex coupled flow, thermal and structural phenomena in parametrized settings.
Reference number: ROMSOC-ESR08
ITMATI, Santiago de Compostela, Spain

Numerical simulations and reduced models of the fluid-structure interaction arising in blood pumps based on wave membranes.
Reference number: ROMSOC-ESR09
Dipartimento di Matematica, Politecnico di Milano, Milan, Italy

Coupled parameterized reduced order modelling of thermo-hydro-mechanical phenomena arising in blast furnaces.
Reference number: ROMSOC-ESR10
Scuola Internazionale Superiore di Studi Avanzati di Trieste (SISSA), Trieste, Italy

Optimal Shape Design of Air Ducts in Combustion Engines.
Reference number: ROMSOC-ESR11
Weierstrass Institute for Applied Analysis and Stochastics, Berlin, Germany

Workshop: “Reducing Dimensions and Cost for UQ in Complex Systems Isaac Newton Institute”

Workshop: Reducing Dimensions and Cost for UQ in Complex Systems
Isaac Newton Institute, Cambridge
5 – 9 March 2018

Registration for this workshop is open through to January 3, 2018.

Francisco Alejandro Díaz De la O, University of Liverpool, UK,
James Gattiker, Los Alamos National Laboratory,
Gianluigi Rozza, SISSA International School for Advanced Studies Trieste, Italy,
Elisabeth Ullmann, Technical University of Munich, Germany,

Uncertainty quantification (UQ) in complex mathematical models is a huge computational challenge for many reasons. Simple UQ tasks such as the estimation of statistical properties of system outputs often require multiple calls to a deterministic solver. A single solver call is already very expensive for complex mathematical models. Advanced UQ tasks such as sensitivity and reliability analysis, parameter identification, or optimal control and design often involve several layers of increasing complexity where each layer requires the performance of a specific UQ task. This workshop will address efficient numerical and statistical methods for reducing the overall cost of solving discrete problems that arise in UQ studies, focusing on methodologies that reduce the dimension of the problems to be solved.

Talks will be organised around topics such as: multilevel and multifidelity methods; reduced basis methods; dimension reduction strategies; low rank and tensor methods; challenges in Gaussian process emulation, and active subspaces. Workshop speakers and participants will be encouraged to explore connections between these topics. There will also be two contributed poster presentations. Please indicate on the registration form if you are interested in presenting a poster. A limited amount of funding may be available to support PhD students.

Please see the workshop webpage soon updated with a list of speakers and tentative titles, and already updated for registration details.

This workshop is the third event in a six-month programme on Uncertainty Quantification at the Isaac Newton Institute:
Organizers: Peter Challenor (University of Exeter), Max Gunzburger (Florida State University), Catherine Powell (University of Manchester), Henry Wynn (London School of Economics)