# inverse problem

Thursday, September 7, 2017 - 1:15pm - 1:50pm

Ville Kolehmainen (University of Eastern Finland)

The approximation error approach was proposed in [J. Kaipio \& E. Somersalo, Statistical and Computational Inverse Problems, Springer, 2004] for handling modelling errors due to model reduction and unknown nuisance parameters in inverse problems. In this talk, we discuss the application of the approximation error approach for approximate marginalization of modelling errors caused by inaccurately known sensor parameters in diffuse optical tomography.

Thursday, March 24, 2016 - 11:00am - 12:00pm

Nicolae Cindea (Blaise Pascal University)

The aim of this talk is to introduce a direct method allowing to solve numerically inverse type problems for linear hyperbolic equations. We first consider the reconstruction of the full solution of the equation from a partial distributed observation. We employ a least-squares technique and minimize the norm of the distance from the observation to any solution. Taking the hyperbolic equation as the main constraint of the problem, the optimality conditions are reduced to a mixed formulation involving both the state to reconstruct and a Lagrange multiplier.

Friday, June 10, 2011 - 9:45am - 10:45am

Christine Shoemaker (Cornell University)

Solving inverse problems for nonlinear simulation models with nonlinear objective is usually a global optimization problem. This talk will present an overview of the development of algorithms that employ response surfaces as a surrogate for an expensive simulation model to significantly reduce the computational effort required to solve continuous global optimization problems and uncertainty analysis of simulation models that require a substantial amount of CPU time for each simulation.