Category Archives: UTOPIAE

Meeting with Partners from DLR (Deutsche Zentrum für Luft- und Raumfahrt) @TH_Koeln @utopiae_network

Dr. Stefan Görtz (Deutschen Zentrum für Luft- und Raumfahrt (DLR), Institut für Aerodynamik und Strömungstechnik) and Christian Sabater (Early Stage Researcher in the UTOPIAE Project) visited the SPOTSeven Lab at TH Köln in Gummersbach. Here are some impressions from our inspiring discussions about uncertainty, reliability, optimization, surrogates, etc.

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In a Nutshell: Sequential Parameter #Optimization is also on @arxiv #rstats @utopiae_network

The paper “In a Nutshell: Sequential Parameter Optimization” has been assigned the permanent arXiv identifier 1712.04076 and is available at:
http://arxiv.org/abs/1712.04076

arXiv:1712.04076
Date: Tue, 12 Dec 2017 00:03:45 GMT   (2255kb,D)
Title: In a Nutshell: Sequential Parameter Optimization
Authors: Thomas Bartz-Beielstein, Lorenzo Gentile, Martin Zaefferer
Categories: cs.MS cs.AI math.OC
Comments: Version 12/2017
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract:
The performance of optimization algorithms relies crucially on their
parameterizations. Finding good parameter settings is called algorithm tuning.
Using a simple simulated annealing algorithm, we will demonstrate how
optimization algorithms can be tuned using the sequential parameter
optimization toolbox (SPOT). SPOT provides several tools for automated and
interactive tuning. The underling concepts of the SPOT approach are explained.
This includes key techniques such as exploratory fitness landscape analysis and
response surface methodology. Many examples illustrate how SPOT can be used for
understanding the performance of algorithms and gaining insight into
algorithm’s behavior. Furthermore, we demonstrate how SPOT can be used as an
optimizer and how a sophisticated ensemble approach is able to combine several
meta models via stacking.

Free Paper: In a Nutshell – Sequential Parameter #Optimization @utopia_network #TH_Koeln #rstats

We proudly present the first result of our cooperation in the UTOPIAE network:
Lorenzo Gentile, Martin Zaefferer, and Thomas Bartz-Beielstein published a paper about sequential parameter optimization. The paper is based on the talk given by Thomas Bartz-Beielstein during the UTOPIAE Network Training School at University of Strathclyde, Glasgow, earlier this year.

The paper is entitled “In a Nutshell: Sequential Parameter Optimization”. It gives a short introduction to surrogate model based optimization, which will be applied in the UTOPIAE project. Many examples illustrate the usefulness of the SPOT approach.

The paper can be downloaded here.

The abstract reads as follows:
The performance of optimization algorithms relies crucially on their parameterizations. Finding good parameter settings is called algorithm tuning. Using a simple simulated annealing algorithm, we will demonstrate how optimization algorithms can be tuned using the sequential parameter opti- mization toolbox (SPOT). SPOT provides several tools for automated and interactive tuning. The underling concepts of the SPOT approach are explained. This includes key techniques such as exploratory fitness landscape analysis and response surface methodology. Many examples illustrate how SPOT can be used for understanding the performance of algorithms and gaining insight into algorithm’s behavior. Furthermore, we demonstrate how SPOT can be used as an optimizer and how a sophisticated ensemble approach is able to combine several meta models via stacking.

@UTOPIAE_network Training School Opened #TH_Koeln

The “Opening Training School” started this morning at the Scottish Universities Insight Institute, University of Strathclyde, Glasgow.
This training school is the first network-wide training event. Lorenzo Gentile and  Dani Irawan, who will be supervised by Prof. Bartz-Beielstein and Prof. Naujoks at TH Köln, are attending this event. More: http://utopiae.eu/2-2/utopiae-training/opening-training-school/

Visit from Italy @UTOPIAE_network

Lorenzo Gentile (left) and Prof. Bartz-Beielstein

Lorenzo Gentile, who will start working in November as a PhD student in the UTOPIAE project, visited Gummersbach.
UTOPIAE is a European research and training network looking at cutting edge methods bridging optimisation and uncertainty quantification applied to aerospace systems. The network will run from 2017 to 2021, and is funded by the European Commission through the Marie Skłodowska-Curie Actions of H2020.

The network is made up of 15 partners across 6 European countries, including the UK, and one international partner in the USA, collecting mathematicians, engineers and computer scientists from academia, industry, public and private sectors.

To train, by research and by example, 15 Early Stage Researchers in the field of uncertainty quantification and optimisation to become leading independent researchers and entrepreneurs that will increase the innovation capacity of the EU.

To equip the researchers with the skills they will need for successful careers in academia and industry. To develop fundamental mathematical methods and algorithms to bridge the gap between Uncertainty Quantification and Optimisation and between Probability Theory and Imprecise Probability Theory for Uncertainty Quantification to efficiently solve high-dimensional, expensive and complex engineering problems.

UTOPIAE M. S. Curie Research Training Network: Early Stage Researcher positions available – Deadline: 16th April 2017

The UTOPIAE (Uncertainty Treatment and OPtimisation In Aerospace Engineering) Marie Sklodowska Curie Research Training Network is currently recruiting 15 Early Stage Researchers to work in the field of Uncertainty Quantification and Optimisation. The list of the available positions and main rules to apply follow below. Recently graduated or graduating students who are interested, find further information on the page http://utopiae.eu/employment-opportunities/
All applications should be emailed to apply@utopiae.eu by Sunday, 16 April 2017.  Continue reading

CfP: Multidisciplinary Design #Optimisation @EUROGEN2017


Across all fields of Engineering Sciences, many design problems are multidisciplinary in nature. An optimal design can be achieved if all the disciplines are concurrently considered in an integrated approach. In MDO the whole is more than the sum of the parts, therefore the optimum of the integrated problem is superior to the design found by optimizing each discipline independently. However, including all disciplines simultaneously significantly increases the complexity of the problem. The optimal design of each discipline can be in itself a hard and computationally intensive optimization problem. In addition, the definition of the level of fidelity of the model for each discipline, the interexchange of variables of different nature (the output of one discipline can become the input to another) and the increased dimensionality, contribute to make the problem considerably harder. The largest number of applications is in the field of aerospace engineering, such as aircraft and spacecraft design in which aerodynamics, structural analysis, propulsion, control theory, and economics are integrated in a single optimization process. But many techniques have been developed and applied in a number of different fields, including automotive design, naval architecture, electronics, computers, and electricity distribution. More: http://eurogen2017.etsiae.upm.es/minisymposia/

CfP: Uncertainty based #Optimization in Engineering @UTOPIAE_network

The deadline to submit your abstract for the sessions on “Uncertainty based Optimization in Engineering” within the framework of the 15th EUROPT Workshop on Advances in Continuous Optimization, Montreal, Canada, 12-14 July 2017, is fast approaching (Abstract submission deadline: March 15, 2017) .

EUROPT website: https://www.gerad.ca/colloques/europt2017/
Special session website: http://utopiae.eu/about/utopiae-training/research/ieee-computational-intelligence-in-aerospace-sciences/ieee-activities/utopiae-activities/

@UTOPIAE_network @TH_Koeln: Uncertainty Treatment and OPtimisation In Aerospace Engineering

Mit “UTOPIAE – Uncertainty Treatment and OPtimisation In Aerospace Engineering” beteiligt sich die TH Köln zum vierten Mal an einem von der EU geförderten Marie-Curie-Innovations-Ausbildungsprogramm. Mit rund 3.9 Millionen Euro Fördersumme forschen europaweit 15 Doktorandinnen und Doktoranden interdisziplinär an der Optimierung der computergenerierten Konstruktion von Luft- und Raumfahrtzeugen.
Koordiniert von der Strathclyde University in Schottland arbeiten insgesamt 15 Hochschulen, Forschungseinrichtungen und Firmen in Großbritannien, Italien, Belgien, Frankreich und den USA zusammen. Darunter die Stanford University, Airbus Operations GmbH und die Deutsche Luft- und Raumfahrt. Die TH Köln übernimmt dabei Aufgabenbereiche aus der Mathematik und Informatik unter der Leitung der Professoren Dr. Thomas Bartz-Beielstein und Dr. Boris Naujoks von der Fakultät für Informatik und Ingenieurwissenschaften.

Den vollständigen Text der Pressemitteilung finden Sie unter https://www.th-koeln.de/hochschule/utopiae–uncertainty-treatment-and-optimisation-in-aerospace-engineering_41808.php