Title: Model reduction and uncertainties in diffuse tomography problems
Organizer(s): Docent Ville Kolehmainen, University of Eastern Finland
Aku Seppänen, University of Eastern Finland
Speakers: Nuutti Hyvönen, Aalto University, Helsinki, Finland
Justification of a point electrode model in electrical impedance tomography

Tanja Tarvainen, University of Eastern Finland, Kuopio, Finland
Corrections to linear methods in diffuse optical tomography using approximation error modelling

Ville Kolehmainen, University of Eastern Finland
Marginalization of uninteresting distributed parameters in inverse problems - Application to diffuse optical tomography

Ting Wei, Lanzhou University, China
Numerical studies for the moving boundary identification in the inverse heat conduction problem
Abstract: Inverse problems are characterized by the property of being extremely sensitive to modeling errors and measurement noise. The modeling errors can arise from reduction of models and unknown/uncertain parameters in the models. Model reduction is an important topic in inverse problems, especially in diffusive tomography problems where the dimensions of the unknowns and forward problems are usually very large. The uncertainty of the parameters can be related, for examples, to partly unknown boundary data or uncertainty of the boundary location. Different model reduction schemes and modeling of uncertainties in diffusive tomography problems are considered in this minisymposium. The problems arising from modeling errors in both deterministic and statistical inversion frameworks are discussed.

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