Springer Time-dependent Problems in Imaging and Parameter Identification

Inverse problems such as imaging or parameter identification deal with the recovery of unknown quantities from indirect observations, connected via a model describing the underlying context.  While traditionally inverse problems are formulated and investigated in a static setting, we observe a significant increase of interest in time-dependence in a growing number of important applications over the last few years. Here, time-dependence affects a) the unknown function to be recovered and / or b) the observed data and / or c) the underlying process. Challenging applications in the field of imaging and parameter identification are techniques such as photoacoustic tomography, elastography, dynamic computerized or emission tomography, dynamic magnetic resonance imaging, super-resolution in image sequences and videos, health monitoring of elastic structures, optical flow problems or magnetic particle imaging to name only a few. Such problems demand for innovation concerning their mathematical description and analysis as well as computational approaches for their solution. From the Back Cover Inverse problems such as imaging or parameter identification deal with the recovery of unknown quantities from indirect observations, connected via a model describing the underlying context.  While traditionally inverse problems are formulated and investigated in a static setting, we observe a significant increase of interest in time-dependence in a growing number of important applications over the last few years. Here, time-dependence affects a) the unknown function to be recovered and / or b) the observed data and / or c) the underlying process. Challenging applications in the field of imaging and parameter identification are techniques such as photoacoustic tomography, elastography, dynamic computerized or emission tomography, dynamic magnetic resonance imaging, super-resolution in image sequences and videos, health monitoring of elastic structures, optical flow problems or magnetic particle imaging to name only a few. Such problems demand for innovation concerning their mathematical description and analysis as well as computational approaches for their solution. About the Author Barbara Kaltenbacher has received her PhD (1996) and her Habilitation (2003) in mathematics at the University of Linz. After professor positions for optimization at the University of Stuttgart and for applied mathematics at the University of Graz, she is professor of Applied Analysis at the University of Klagenfurt since 2011. Her reseach interests lie in the field of inverse problems, in particular regularization methods and parameter identification in partial differential equations, as well as in modeling, for example in nonlinear acoustics and piezoelectricity. She has co-authored tree research monographs and published more than 100 refereed journal and proceedings papers. This work has been supported by the Austrian and the German Science Foundations, as well as by industrial partners in a number of projects. She currently serves as an editorial board member for Inverse Problems and the IMA Journal of Numerical Analysis as well as an editor in chief of the Journal of the European Mathematical Society. Thomas Schuster studied mathematics and computer science at Saarland University in Saarbrücken where he also got his PhD (1999) and Habilitation (2004). After positions as a Visiting Assistant Professor at Tufts University Medford, MA, USA, as an Associate Professor for Applied Mathematics at Helmut Schmidt University Hamburg, as a Full Professor for Numerical Mathematics in Oldenburg he holds since 2012 a Chair as Full Professor for Numerical Mathematics at Saarland University in Saarbrücken. His research are theoretical foundations and applications for inverse and ill-posed problems. Besides the extension of regularization theory from classical settings to Banach spaces, he currently investigates the app