Inverse problems with imperfect forward modelsMS72

Inverse problems are typically concerned with the interpretation of indirectly measured data that are related to the quantities of interest (images) through models that describe the data acquisition. In practice, one has to deal not only with noisy or incomplete data, but also with simplified or imperfect models that cannot capture the mechanisms of data acquisition in full complexity. Correctly accounting for these imperfections is crucial for the development of stable numerical algorithms. The aim of this workshop is bringing together researchers working on different approaches to such problems arising in imaging in order to highlight similarities as well as differences and foster further collaboration.

A lattice analogue of the residual method for inverse problems with imperfect forward models
Yury Korolev (University of Cambridge)
All-at-once formulation and regularization of inverse problems
Barbara Kaltenbacher (Alpen-Adria-Universität Klagenfurt)
Deep learning for trivial inverse problems
Peter Maass (University of Bremen)
Spatio-temporal imaging by joint motion and image reconstruction and its application to spat-temporal MRI
Angelica I. Aviles-Rivero (University of Cambridge)
Bayesian imaging inverse problems with partially unknown models
Marcelo Pereyra (Harriott-Watt University)
Improved source estimation in EEG with Bayesian modelling of the unknown skull conductivity
Alexandra Koulouri (Aristotle University of Thessaloniki)
A time-regularized blind deconvolution method via non-convex optimisation
Audrey Repetti (Heriot-Watt University, Edinburgh)
Accounting for model-errors in PDE-constrained optimization
Tristan van Leeuwen (Utrecht University)
Martin Burger (University of Muenster)
Yury Korolev (University of Cambridge)
image reconstruction, inverse problems, partial differential equation models