Guy Chavent – författare

Inverse problems in wave propagation concern extraction of information about distant structural features from the measurements of scattered waves. Tasks of this nature arise in geophysics, ocean acoustics, civil and environmental engineering, ultrasonic nondestructive testing, biomedical ultrasonics, radar, astrophysics, and other areas of science and technology. The papers in this volume represent most of these scientific and technical topics, together with fundamental mathematical investigations of the relation between waves and scatterers.

Inverse problems in wave propagation concern extraction of information about distant structural features from the measurements of scattered waves. Tasks of this nature arise in geophysics, ocean acoustics, civil and environmental engineering, ultrasonic nondestructive testing, biomedical ultrasonics, radar, astrophysics, and other areas of science and technology. The papers in this volume represent most of these scientific and technical topics, together with fundamental mathematical investigations of the relation between waves and scatterers.

Inverse problems in wave propagation concern extraction of information about distant structural features from the measurements of scattered waves. Tasks of this nature arise in geophysics, ocean acoustics, civil and environmental engineering, ultrasonic nondestructive testing, biomedical ultrasonics, radar, astrophysics, and other areas of science and technology. The papers in this volume represent most of these scientific and technical topics, together with fundamental mathematical investigations of the relation between waves and scatterers.

This book describes the state of the art in the field of modeling and solving numerically inverse problems of wave propagation and diffraction. It addresses mathematicians, physicists and engineers as well. Applications in such fields as acoustics, optics, and geophysics are emphasized. Of special interest are the contributions to two and three dimensional problems without reducing symmetries. Topics treated are the obstacle problem, scattering by classical media, and scattering by distributed media.

The domain of inverse problems has experienced a rapid expansion, driven by the increase in computing power and the progress in numerical modeling. When I started working on this domain years ago, I became somehow fr- tratedtoseethatmyfriendsworkingonmodelingwhereproducingexistence, uniqueness, and stability results for the solution of their equations, but that I was most of the time limited, because of the nonlinearity of the problem, to provethatmyleastsquaresobjectivefunctionwasdi?erentiable....Butwith my experience growing, I became convinced that, after the inverse problem has been properly trimmed, the ?nal least squares problem, the one solved on the computer, should be Quadratically (Q)-wellposed,thatis,both we- posed and optimizable: optimizability ensures that a global minimizer of the least squares function can actually be found using e?cient local optimization algorithms, and wellposedness that this minimizer is stable with respect to perturbation of the data. But the vast majority of inverse problems are nonlinear, and the clas- cal mathematical tools available for their analysis fail to bring answers to these crucial questions: for example, compactness will ensure existence, but provides no uniqueness results, and brings no information on the presence or absenceofparasiticlocalminimaorstationarypoints....

The domain of inverse problems has experienced a rapid expansion, driven by the increase in computing power and the progress in numerical modeling. When I started working on this domain years ago, I became somehow fr- tratedtoseethatmyfriendsworkingonmodelingwhereproducingexistence, uniqueness, and stability results for the solution of their equations, but that I was most of the time limited, because of the nonlinearity of the problem, to provethatmyleastsquaresobjectivefunctionwasdi?erentiable....Butwith my experience growing, I became convinced that, after the inverse problem has been properly trimmed, the ?nal least squares problem, the one solved on the computer, should be Quadratically (Q)-wellposed,thatis,both we- posed and optimizable: optimizability ensures that a global minimizer of the least squares function can actually be found using e?cient local optimization algorithms, and wellposedness that this minimizer is stable with respect to perturbation of the data. But the vast majority of inverse problems are nonlinear, and the clas- cal mathematical tools available for their analysis fail to bring answers to these crucial questions: for example, compactness will ensure existence, but provides no uniqueness results, and brings no information on the presence or absenceofparasiticlocalminimaorstationarypoints....