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SUMMARY:Uncertainty quantification @ MdS
DTSTART;VALUE=DATE-TIME:20160517T073000Z
DTEND;VALUE=DATE-TIME:20160519T153000Z
DTSTAMP;VALUE=DATE-TIME:20220123T182116Z
UID:indico-event-451@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 probaility and statistics\n- Genera
l introduction to the software tools: OpenTurns and Uranie\n \nDay 2\n- S
tatistical estimation: parametric and non-parametric\, testing\n- Modeling
with non-numerical data: expert judgement\, entropy\n- Central trend: loc
al and gloal sensitivity indices (design of experiments\, sampling\, Sool
indices)\n- computing the probability of rare events\, simulation methods\
, FORM?SOEM\n \nDay 3\n- Distributed computing: parallel solvers\, batch
jobs submission on a parallel computer\, implementation within OpenTurns /
Salomeie\nand Uranie\n- Introduction to meta-model building\, least-squar
es\, other response surface\, Krieging\, neural networks\n- Introduction t
o polynomial chaos\n\n\nLearning outcomes\nLearn to recognize when uncerta
inty quantification can bring new insight to simulations.\nKnow the main t
ools and techniques to investigate uncertainty propagation.\nGain familiar
ity 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/eve
nt/451/
LOCATION:Maison de la simulation
URL:https://events.prace-ri.eu/event/451/
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