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DTSTAMP:20260824T221709Z
UID:7b430bf1-ca0c-4efe-82d9-1177868aa287
DTSTART:20220509T073000Z
DTEND:20220523T150000Z
DESCRIPTION:The cours will be held online may the 9th\, the 16th and the 23
 rd.\n\nUncertainty in computer simulations\, deterministic and probabilist
 ic methods for quantifying uncertainty\, OpenTurns software\, Uranie softw
 are\n\nContent :\nUncertainty quantification takes into account the fact t
 hat most inputs to a simulation code are only known imperfectly. It seeks 
 to translate this uncertainty of the data to improve the results of the si
 mulation. 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 in t
 he subject\, as it provides the computing power made necessary by the larg
 e number of simulations needed.\nThe course will present the most importan
 t theoretical tools for probability and statistical analysis\, and will il
 lustrate the concepts using the OpenTurns software.\n\nCourse Outline :\n\
 nDay 1 : Methodology of Uncertainty Treatment – Basics of Probability a
 nd Statistics\n\n\n	\n	General Uncertainty Methodology (30’) : A. Dutf
 oy (G. Blondet)\n	\n	\n	Probability and Statistics: Basics (45’) : G. B
 londet (A. Dutfoy)\n	\n	\n	General introduction to Open TURNS and Uranie (
 2 * 30’) : G. Blondet\, JB Blanchard\n	\n	\n	Introduction to Python and
  Jupyter (45’): practical work on distributions manipulations: G. Blonde
 t (M. Baudin)\n	\n\n\nLunch\n\n\n	\n	Uncertainty Quantification (45’) :
  JB Blanchard (G. Blondet)\n	\n	\n	OpenTURNS – Uranie practical works: s
 ections 1\, 2 (1h): JB Blanchard \, G. Blondet\, (A. Dutfoy)\n	\n	\n	Centr
 al tendency and Sensitivity analysis (1h): A. Dutfoy (M. Baudin)\n	\n\n\n
 Day 2 : Quantification\, Propagation and Ranking of Uncertainties\n\n\n	\
 n	Application to OpenTURNS and Uranie (1h): M. Baudin\, G. Blondet\, JB Bl
 anchard\n	\n	\n	Estimation of probability of rare events (1h): G. Blondet
  (M. Baudin\, JB Blanchard)\n	\n	\n	Application to OpenTURNS and Uranie (1
 h): M. Baudin\, G. Blondet\, JB Blanchard\n	\n\n\nLunch\n\n\n	\n	Distribut
 ed computing (1h) : Uranie (15’\, JB Blanchard)\, OpenTURNS (15’\, G.
  Blondet)\, Salome et OpenTURNS (30’\, O. Mircescu)\n	\n	\n	Optimisation
  and Calibration (1h) : JB Blanchard \, M. Baudin\, (G. Blondet)\n	\n	\n	
 Application to OpenTURNS and Uranie (1h): JB Blanchard\, M. Baudin\, G. Bl
 ondet\n	\n\n\nDay 3 : HPC aspects – Meta model\n\n\n	\n	HPC aspects spe
 cific to the Uncertainty treatment (1h) : K. Delamotte (M. Baudin)\n	\n	
 \n	Introduction to Meta models (validation\, over-fitting) – Polynomial 
 chaos expansion\, Kriging meta model (2h) : C. Mai\, M. Baudin\n	\n\n\nLu
 nch\n\n\n	\n	Application to OpenTURNS and Uranie (2h) : M. Baudin\, C. Ma
 i\, G. Blondet\, JB. Blanchard\n	\n	\n	Discussion / Participants projects\
 n	\n\n\nLearning outcomes :\nLearn to recognize when uncertainty quantific
 ation can bring new insight to simulations.\nKnow the main tools and techn
 iques to investigate uncertainty propagation.\nGain familiarity with moder
 n tools for actually carrying out the computations in a HPC context.\n\nPr
 erequisites :\nBasic knowledge of probability will be useful\, as will a b
 asic familiarity with Linux.\n\nLecturers :\n\n\n	CEA : J.B. Blanchard\, F
 . Gaudier\n	EDF : M. Baudin\, A. Dutfoy\, C. Mai\, O. Mircescu\n	IMACS : K
 . Delamotte\n	PhiMeca : G. Blondet\n\nhttps://events.prace-ri.eu/event/128
 7/
SUMMARY:[ONLINE] HPC and Uncertainty Treatment - Examples with OpenTURNS an
 d Uranie @ MdlS
URL;VALUE=URI:https://events.prace-ri.eu/event/1287/
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