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SUMMARY:Large Deviation Theory for Stochastic Partial Differential Equatio
 ns: Modeling and Computational Aspects - Eric Vanden-Eijnden (Courant Inst
 itute\, NYU)
DTSTART:20140605T140000Z
DTEND:20140605T150000Z
UID:TALK52855@talks.cam.ac.uk
CONTACT:Carola-Bibiane Schoenlieb
DESCRIPTION:I will explain how Large DeviationTheory (LDT) can be used to 
 estimate various expectations over probability distributions of the soluti
 ons of stochastic partial differential equations (SPDEs) that arises e.g. 
 in material sciences\, fluid dynamics\, and atmosphere/ocean science.  In 
 particular\, I will show how scaling arguments made within the realm of LD
 T sometime permits to obtain useful prior information about the system’s
  behavior. I will also illustrate via examples that LDT enable calculation
 s that are mostly out of reach of brute force simulations.
LOCATION:MR 14\, CMS
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