The beta version of UQLab, the Matlab-based platform for uncertainty quantification, has been released on July 1st, 2015. After 4 months of existence, UQLab has been downloaded already by more than 150 people from 23 countries. The feedback we got so far being very positive, it is my pleasure to further advertise UQLab through this message.
The UQLab platform contains state-of-the algorithms gathered into modules that allow you to perform uncertainty propagation through computational models (Monte Carlo and derived simulation methods, polynomial chaos expansions), sensitivity analysis (Morris method, Sobol' indices), and build surrogate models for general use (Gaussian processes, a.k.a Kriging). Rare event simulation (structural reliability methods) will be available soon. Connections to third party codes are easier than ever.
UQLab-beta is freely licensed to academic users (universities and public research institutions).
Visit our website www.uqlab.com. Register, download, enjoy !
Prof. Bruno SUDRET, ETH Zurich
On behalf of the UQLab Development Team
It is our pleasure to invite you to submit an abstract for the mini-symposium MS 1307 entitled “Non-intrusive surrogate models for uncertainty quantification in high dimensions”, see details below.
This MS is organised as part of the ECCOMAS Congress 2016 (VII European Congress on Computational Methods in Applied Sciences and Engineering) to be held in Crete, Greece, in June 5-10, 2016 (http://www.eccomas2016.org/ ).
We kindly invite you to submit abstracts to our mini-symposium by November 29, 2015 (see instructions below). Authors of accepted abstracts will be invited to submit their full paper by February 29, 2016.
Looking forward to receiving your contributions
Bruno Sudret, ETH Zurich, Switzerland,
Eleni Chatzi, ETH Zurich, Switzerland,
Jean-Marc Bourinet, IFMA Clermont Ferrand, France
Abstract -- MS 1307 -- Non-intrusive surrogate models for uncertainty quantification in high dimensions
Uncertainty quantification has become a key challenge in modern engineering, whether it is used for assessing the safety of systems (structural reliability methods), for finding the distributions and moments of quantities of interest, for determining the key parameters of the problem (sensitivity analysis), or for optimizing under safety constraints (reliability-based design optimization - RBDO). Various techniques for solving these problems have received much attention in the mechanical, civil, and aerospace engineering communities over the past two decades.
However, accurate computational models (e.g., finite element analysis) of complex structures or systems are often costly. A single run of the model may last minutes to hours, even on powerful computers. In order to use these models in analyses that require repeated calls to the computer code, it is necessary to develop a substitute that may be evaluated thousands to millions of times at low cost: these substitutes are referred to as meta-models or surrogate models.
The “curse of dimensionality”, i.e. the exploding complexity observed when the number of input variables increases, is a recurrent problem in surrogate modeling. The aim of this mini-symposium is to confront various kinds of meta-modeling techniques in the context of uncertainty propagation, including polynomial chaos expansions, Kriging, support vector regression, etc. and to discuss the recent advances towards solving high dimensional problems.
Instructions for Abstract Submission
In order to submit your abstract, first you need to register on the Congress website. After completing this pre-registration form (alternatively click Register at the top-right of the Congress webpage), you will receive an email to validate your Account. After validation, you will receive a second email with your Username and password, which will be used to login to your ECCOMAS Congress 2016 Account.
From your Account Overview Menu click My Abstracts / My Papers and then click Submit an abstract. Choose Submit to Minisymposium, then Select MS MS 1307 - Non-intrusive surrogate models for uncertainty quantification in high dimensions and proceed with the Abstract Submission.
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