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CATEGORIES:Isaac Newton Institute Seminar Series
SUMMARY:Dynamic discretization of inverse problems using h
 ierarchical Bayesian models - Erkki Somersalo (Cas
 e Western Reserve University)
DTSTART;TZID=Europe/London:20230622T100000
DTEND;TZID=Europe/London:20230622T110000
UID:TALK200446AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/200446
DESCRIPTION:Estimating distributed parameters from indirect no
 isy observations requires a discretization of the 
 unknown quantity to make the forward model computa
 tionally feasible. In ill-posed problems\, the mod
 eling error due to the discretization may have an 
 adverse impact on the solution if not taken into a
 ccount properly\, in particular\, when the&nbsp\; 
 quality of the data is high and the modeling error
  dominates the noise. To minimize the effect of th
 e modeling error\, refinement of the discretizatio
 n is an option that may increase significantly the
  computational cost. Computational efficiency may 
 be increased by selectively refining the discretiz
 ation only where needed\, and by using anisotropic
  discretization. In this talk\, the problem is add
 ressed by defining the discretization in terms of 
 a metric that is coupled to the unknown distribute
 d parameter through a Bayesian hypermodel\, thus m
 aking the discretization part of the inverse probl
 em. Computed examples of this coupled problem incl
 ude a sparse view tomography problem.
LOCATION:Seminar Room 1\, Newton Institute
CONTACT:
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