Nuclear Engineering Commons^™

An Information Approach To Regularization Parameter Selection For The Solution Of Ill-Posed Inverse Problems Under Model Misspecification, Aleksey M. Urmanov

Doctoral Dissertations

Engineering problems are often ill-posed, i.e. cannot be solved by conventional data-driven methods such as parametric linear and nonlinear regression or neural networks. A method of regularization that is used for the solution of ill-posed problems requires an a priori choice of the regularization parameter. Several regularization parameter selection methods have been proposed in the literature, yet, none is resistant to model misspecification. Since almost all models are incorrectly or approximately specified, misspecification resistance is a valuable option for engineering applications.

Each data-driven method is based on a statistical procedure which can perform well on one data set and can …