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SUMMARY:Big data and uncertainty quantification: statistical inference and
  information-theoretic techniques applied to computational chemistry
DTSTART:20190326T080000
DTEND:20190403T180000
DTSTAMP:20260501T210534Z
UID:7959721c00c7708fe7b4c5b8d57a930b879272206d12b832db85cecb
CATEGORIES:Conferences - Seminars
DESCRIPTION:Carsten Hartmann\, BTU Cottbus-Senftenberg Fabio Nobile\, EP
 FL Frank Pinski\, University of Cincinnati Tim Sullivan\, Zuse Institute
  Berlin\nAn incentive to use coarse-grained models is to use them for infe
 rence and control instead of the original (often intractable) model. Since
  coarse-grained models are always “wrong”\, questions such as inferenc
 e under model misspecification or goal-oriented uncertainty quantification
  (e.g. for control) come into play. This workshop will address such topics
 \, with a special focus on predictive modelling\, uncertainty quantificati
 on in molecular simulation and sensitivity analysis.\n\n26 to 29 March 201
 9 - CIB premises (Room GA 3 21).\n1 to 3 April 2019 - CECAM premises (Room
  BCH 3113).\n\nPart of the Semester : Multi-scale Mathematical Modelling 
 and Coarse-grain Computational Chemistry
LOCATION:GA 3 21 https://plan.epfl.ch/?room=GA321
STATUS:CONFIRMED
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