#### Location

Brunswick, ME

#### Start Date

1-1-1992 12:00 AM

#### Description

Solution to inverse problems is of interest in many fields of science and engineering. In nondestructive evaluation [1], for example, inverse techniques are used to obtain quantitative estimates of the size, shape and nature of defects in materials. Inv.:rse scattering problems in electromagnetics deal with estimation of scatterer information from knowledge of incident and scattered fields. Inverse problems are frequently described by Fredholm integral equations in the form 1 ∫bak(x,y)z(y)dy=u(x)(c⩽x⩽d) where u(x) represents the measured data, z(y) represents the source function or the system states or parameters, and k(x,y) represents the kernel of the transformation. The objective of inverse problem is then to solve for the source or state function from known measurements. This problem is sensitive to the system parameters z, to the shape of the kernel k, and to the accuracy of the measurements u.

#### Book Title

Review of Progress in Quantitative Nondestructive Evaluation

#### Volume

11A

#### Chapter

Chapter 3: Interpretive Signal Processing and Image Reconstruction

#### Section

Neural Networks

#### Pages

709-716

#### DOI

10.1007/978-1-4615-3344-3_91

#### Copyright Owner

Springer-Verlag US

#### Copyright Date

January 1992

#### Language

en

#### File Format

application/pdf

A Neural Network Approach for Solving Inverse Problems in NDE

Brunswick, ME

Solution to inverse problems is of interest in many fields of science and engineering. In nondestructive evaluation [1], for example, inverse techniques are used to obtain quantitative estimates of the size, shape and nature of defects in materials. Inv.:rse scattering problems in electromagnetics deal with estimation of scatterer information from knowledge of incident and scattered fields. Inverse problems are frequently described by Fredholm integral equations in the form 1 ∫bak(x,y)z(y)dy=u(x)(c⩽x⩽d) where u(x) represents the measured data, z(y) represents the source function or the system states or parameters, and k(x,y) represents the kernel of the transformation. The objective of inverse problem is then to solve for the source or state function from known measurements. This problem is sensitive to the system parameters z, to the shape of the kernel k, and to the accuracy of the measurements u.