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DTSTAMP:20260708T100134Z
UID:32dd560a-c22f-4a31-bad5-23c58f303693
DTSTART:20190527T073000Z
DTEND:20190529T150000Z
DESCRIPTION:Uncertainty in computer simulations\, deterministic and probabi
 listic methods for quantifying uncertainty\, OpenTurns software\, Uranie s
 oftware\n\nContent\nUncertainty quantification takes into account the fact
  that most inputs to a simulation code are only known imperfectly. It seek
 s to translate this uncertainty of the data to improve the results of the 
 simulation. This training will introduce the main methods and techniques b
 y which this uncertainty propagation can be handled without resorting to a
 n exhaustive exploration of the data space. HPC plays an important role in
  the subject\, as it provides the computing power made necessary by the la
 rge number of simulations needed.\nThe course will present the most import
 ant theoretical tools for probability and statistical analysis\, and will 
 illustrate the concepts using the OpenTurns software.\n\nCourse Outline\nD
 ay 1\n- General methodology for handling uncertainty\, presentation of a c
 ase study\n- Fundamental notions from probability 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\, Sobol
  indices)\n- computing the probability of rare events\, simulation methods
 \, FORM/SORM\n \nDay 3\n- Distributed computing: parallel solvers\, batch
  jobs submission on a parallel computer\, implementation within OpenTurns 
 / Salome\nand Uranie\n- Introduction to meta-model building\, least-square
 s\, other response surface\, Kriging\, neural networks\n- Introduction to 
 polynomial chaos\n\n\nLearning outcomes\nLearn to recognize when uncertain
 ty quantification can bring new insight to simulations.\nKnow the main too
 ls and techniques to investigate uncertainty propagation.\nGain familiarit
 y with modern tools for actually carrying out the computations in a HPC co
 ntext.\n\nPrerequisites\nBasic knowledge of probability will be useful\, a
 s will a basic familiarity with Linux.\nhttps://events.prace-ri.eu/event/8
 15/
SUMMARY:Uncertainty quantification @MdlS
URL;VALUE=URI:https://events.prace-ri.eu/event/815/
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