IDENTIFICATION OF SPATIALLY-VARYING PARAMETERS IN DISTRIBUTED PARAMETER SYSTEMS BY DISCRETE REGULARIZATION.
Conference Paper
Overview
Overview
abstract
Identification of spatially-varying parameters in distributed parameter systems from noisy data is an ill-posed problem. The regularization identification approach, provides stable approximate solutions to that problem. In this work, a discretized minimization of the smoothing functional is proposed by using finite-dimensional convergent approximations in Sobolev spaces. A convergence theorem for the discretized minimization of the smoothing functional is established. The performance of this discrete regularization approach is evaluated by numerical experiments on the identification of spatially-varying diffusivity in the two-dimensional diffusion equation.
