Oberwolfach Reports


Full-Text PDF (2670 KB) | Introduction as PDF | Metadata | Table of Contents | OWR summary
Volume 14, Issue 2, 2017, pp. 1463–1549
DOI: 10.4171/OWR/2017/24

Published online: 2018-04-27

Computational Inverse Problems for Partial Differential Equations

Liliana Borcea[1], Thorsten Hohage[2] and Barbara Kaltenbacher[3]

(1) University of Michigan, Ann Arbor, USA
(2) Georg-August-Universität Göttingen, Germany
(3) Universität Alpen-Adria, Klagenfurt, Austria

The problem of determining unknown quantities in a PDE from measurements of (part of) the solution to this PDE arises in a wide range of applications in science, technology, medicine, and finance. The unknown quantity may e.g. be a coefficient, an initial or a boundary condition, a source term, or the shape of a boundary. The identification of such quantities is often computationally challenging and requires profound knowledge of the analytical properties of the underlying PDE as well as numerical techniques. The focus of this workshop was on applications in phase retrieval, imaging with waves in random media, and seismology of the Earth and the Sun, a further emphasis was put on stochastic aspects in the context of uncertainty quantification and parameter identification in stochastic differential equations. Many open problems and mathematical challenges in application fields were addressed, and intensive discussions provided an insight into the high potential of joining deep knowledge in numerical analysis, partial differential equations, and regularization, but also in mathematical statistics, homogenization, optimization, differential geometry, numerical linear algebra, and variational analysis to tackle these challenges.

No keywords available for this article.

Borcea Liliana, Hohage Thorsten, Kaltenbacher Barbara: Computational Inverse Problems for Partial Differential Equations. Oberwolfach Rep. 14 (2017), 1463-1549. doi: 10.4171/OWR/2017/24