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SUMMARY:Uncertainty Quantification of geochemical and mechanical compactio
 n in layered sedimentary basins - Lorenzo Tamellini (Istituto di Matematic
 a Applicata e Tecnologie Informatiche (IMATI))
DTSTART:20180214T110000Z
DTEND:20180214T130000Z
UID:TALK100720@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:This presentation is joint work of:  &nbsp\;  Ivo Colombo\, Di
 partimento di Ingegneria Civile e Ambientale\, Politecnico di Milano\, Ita
 ly  Fabio Nobile\, CSQI-MATHICSE\, Ecole Polytechnique F&eacute\;d&eacute\
 ;rale de Lausanne\, Switzerland  Giovanni Porta\, Dipartimento di Ingegner
 ia Civile e Ambientale\, Politecnico di Milano\, Italy  Anna Scotti\, MOX\
 , Dipartimento di Matematica\, Politecnico di Milano\, Italy  Lorenzo Tame
 llini\, CNR - Istituto di Matematica Applicata e Tecnologie Informatiche &
 ldquo\;E. Magenes&rdquo\;\, Pavia\, Italy  &nbsp\;  &nbsp\;  In this work 
 we propose an Uncertainty Quantification methodology for the evolution of 
 sedimentary basins undergoing mechanical and geochemical compaction proces
 ses\, which we model as a coupled\, time-dependent\, non-linear\, monodime
 nsional (depth-only) system of PDEs with uncertain parameters.   &nbsp\;  
 Specifically\, we consider multi-layered basins\, in which each layer is c
 haracterized by a different material. The multi-layered structure gives ri
 se to discontinuities in the dependence of the state variables on the unce
 rtain parameters. Because of these discontinuites\, an appropriate treatme
 nt is needed for surrogate modeling techniques such as sparse grids to be 
 effective.   &nbsp\;  To this end\, we propose a two-steps methodology whi
 ch relies on a change of coordinate system to align the discontinuities of
  the target function within the random parameter space. Once this aligneme
 nt has been computed\, a standard sparse grid approximation of the state v
 ariables can be performed. The effectiveness of this procedure is due to t
 he fact that the physical locations of the interfaces among layers feature
  a smooth dependence on the random parameters and are therefore amenable t
 o sparse grid polynomial approximations.  &nbsp\;  We showcase the capabil
 ities of our numerical methodologies through some synthetic test cases.   
 &nbsp\;  <br><br><br>
LOCATION:Seminar Room 2\, Newton Institute
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