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SUMMARY:Uncertainty quantification @ MdlS
DTSTART;VALUE=DATE-TIME:20180516T073000Z
DTEND;VALUE=DATE-TIME:20180518T153000Z
DTSTAMP;VALUE=DATE-TIME:20190318T140637Z
UID:indico-event-680@events.prace-ri.eu
DESCRIPTION:Uncertainty in computer simulations\, deterministic and probab
ilistic methods for quantifying uncertainty\, OpenTurns software\, Uranie
software\n\nContent\nUncertainty quantification takes into account the fac
t that most inputs to a simulation code are only known imperfectly. It see
ks to translate this uncertainty of the data to improve the results of the
simulation. This training will introduce the main methods and techniques
by which this uncertainty propagation can be handled without resorting to
an exhaustive exploration of the data space. HPC plays an important role i
n the subject\, as it provides the computing power made necessary by the l
arge number of simulations needed.\nThe course will present the most impor
tant theoretical tools for probability and statistical analysis\, and will
illustrate the concepts using the OpenTurns software.\n\nCourse Outline\n
Day 1\n- General methodology for handling uncertainty\, presentation of a
case study\n- Fundamental notions from probability and statistics\n- Gener
al introduction to the software tools: OpenTurns and Uranie\n \nDay 2\n-
Statistical estimation: parametric and non-parametric\, testing\n- Modelin
g with non-numerical data: expert judgement\, entropy\n- Central trend: lo
cal and gloal sensitivity indices (design of experiments\, sampling\, Sobo
l indices)\n- computing the probability of rare events\, simulation method
s\, FORM/SORM\n \nDay 3\n- Distributed computing: parallel solvers\, batc
h jobs submission on a parallel computer\, implementation within OpenTurns
/ Salomeie\nand Uranie\n- Introduction to meta-model building\, least-squ
ares\, other response surface\, Kriging\, neural networks\n- Introduction
to polynomial chaos\n\n\nLearning outcomes\nLearn to recognize when uncert
ainty quantification can bring new insight to simulations.\nKnow the main
tools and techniques to investigate uncertainty propagation.\nGain familia
rity with modern tools for actually carrying out the computations in a HPC
context.\n\nPrerequisites\nBasic knowledge of probability will be useful\
, as will a basic familiarity with Linux.\n\nhttps://events.prace-ri.eu/ev
ent/680/
LOCATION:Maison de la simulation
URL:https://events.prace-ri.eu/event/680/
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