The 13th International Conference on Applications of Statistics and Probability in Civil Engineering (ICASP13) will be held in Seoul, South Korea on May 26-30, 2019 (http://www.icasp13.snu.ac.kr/). As part of this conference, it is our pleasure to invite you to contribute to our Mini-Symposium “Surrogate models for uncertainty quantification, reliability and sensitivity analysis” (MS31), see details below.
The deadline for submitting abstracts is April 30, 2018. Please refer to the guidelines of the conference (https://www.icasp13.snu.ac.kr/submission) when preparing your abstract.
We hope that you will be able to contribute to this mini-symposium and we look forward to seeing you in Seoul next year.
Sudret Bruno <firstname.lastname@example.org>
Structural reliability methods and more generally, methods that aim at taking into account model- and parameters uncertainty have received much attention in the mechanical, civil, and aerospace engineering communities over the past two decades. Some well-known methods such as FORM/SORM for reliability analysis, spectral methods for stochastic finite element analysis, global sensitivity analysis (Sobol’ indices), etc. are nowadays applied in an industrial context, e.g. nuclear, aerospace, and automotive industries, among others.
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 for reliability analysis and reliability-based design optimization, which require repeated calls to the computational 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 aim of this mini-symposium is to confront various kinds of meta-modeling techniques in the context of uncertainty propagation including classical response surfaces, polynomial chaos expansions, Kriging, support vector regression, neural networks, sparse grid interpolation, etc. Papers that present new methodology developments as well as large scale industrial applications that make use of surrogate models are welcome.