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DTSTAMP:20260522T150122Z
LOCATION:C2/3/4 Ballroom
DTSTART;TZID=America/Chicago:20181113T083000
DTEND;TZID=America/Chicago:20181113T170000
UID:submissions.supercomputing.org_SC18_sess322_post165@linklings.com
SUMMARY:Making Sense of Scientific Simulation Ensembles
DESCRIPTION:Mai Dahshan and Nicholas Polys (Virginia Tech)\n\nScientists r
 un many simulations with varying initial conditions, known as "ensembles",
  to understand the influence and relationships among multiple parameters o
 r ensemble members. Most of the ensemble visualization and analysis approa
 ches and techniques focus on analyzing the relationships between either th
 e ensemble members or output parameter space while neglecting the effect o
 f input parameters and humans in the analysis loop. Therefore, we develope
 d an approach to the visual analysis of scientific data that merges human 
 expertise and intuition with machine learning and statistics allowing scie
 ntists to explore, search, filter, and make sense of their high dimensiona
 l ensemble. Our tool,  "GLEE" (Graphically-Linked Ensemble Explorer), is a
 n interactive visualization tool that consists of three visual views: Ense
 mble View, Parameter View, and Statistical View. Each view offers differen
 t functionality for exploration and interoperation of the relations and co
 rrelations between different runs, a subset of runs, and input and output 
 parameters.\n\nRegistration Category: Tech Program Reg Pass, Exhibits Reg 
 Pass\n\n
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