# inverse problem

Monday, April 29, 2019 - 2:40pm - 3:30pm

Francis Chung (University of Kentucky)

The acousto-optic effect is a phenomenon in which the presence of an acoustic wave changes the light transmission properties of an optical medium. Acousto-optic tomography (AOT) is a hybrid imaging method which takes advantage of this effect. The general idea is to modulate the optical properties of an optical medium by varying an acoustic wave, take optical measurements for each modulation, and use this enormous trove of data to solve a stable inverse problem.

Monday, April 29, 2019 - 3:40pm - 4:30pm

Weiran Sun (Simon Fraser University)

A classical approach in identifying optical parameters in radiative transfer equations is to apply the singular decomposition to their associated Albedo operators. This approach heavily relies on the specific structure of the equation, and thus could be hard to apply to nonlinear equations. In this talk, we show that by making use of classical analytical tools for kinetic equations, we can recover the absorption and scattering coefficients of a class of equations without resorting to fine details of the Albedo operator.

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.