6th Dutch Inverse Problems Meeting
The meeting will take place on 29-30 October 2026 at CWI in Amsterdam. More details on the program will be added in due course. Registration will open soon. We expect that registration costs will be similar to last year (EUR 45 per day + 40 for dinner on Thursday).
Program
Thursday: Masterclass on numerical linear algebra for inverse problems
10:30 - 11:30: Masterclass part I - Krylov subspace methods for regularization in inverse problems by Maike Meier (RUG)
Krylov subspace methods have long been used in inverse problems for iterative or Tikhonov regularization. Recently, there has been renewed attention for the applicability of these algorithms for more computationally challenging types of regularization as total variation. The linear algebraic interpretation of Krylov methods offer one big upside compared to standard optimization tools: the automatic determination of a regularization parameter via hybrid Krylov subspace methods. This talk will introduce these algorithms and demonstrate their performance on large-scale, 3D image reconstruction problems. Finally, we will shortly touch on the Bayesian interpretation and the use of Krylov methods for uncertainty quantification.
11:30 - 12:30: Masterclass part II - Numerical linear algebra in data assimilation by Jemima Tabeart (TU/e)
Data assimilation methods are a particular class of inverse problem where prior information from a dynamical system is combined with observation information to obtain an improved estimate of the state at a given time. I will introduce the variational data assimilation problem, which gives rise to a nonlinear least squares problem. We will investigate how techniques from numerical linear algebra (different solvers, parallelisation, preconditioners) can be used to `solve’ a high-dimensional ill-conditioned problem in a very short wallclock time.
12:30 - 14:00: Lunch
14:00 - 16:00: Lab tour
16:00 - 18:00: Poster session and drinks
18:30 - 20:00: Dinner (at CWI)
Friday
09:30 – 10:30: Key note by Martin Benning (UCL London) - Mind the Gap: Bilevel Optimisation with Fenchel-Young Functions.
10:30 – 11:00: Coffee break
11:00 – 12:30: Invited talks I
David Nolte (RUG) - Parameter estimation in coupled fluid-solid systems with application to cardiac biomechanics
Svetlana Dubinkina (VU) - TBA
Carlas Sierd Smith (TU Delft) - Super-resolution microscopy
12:30 – 13:30: Lunch break
13:30 – 14:30: Invited talks II
Emily Anne Frame (CWI) - Neutron imaging for cultural heritage
Mark Peletier (TU Eindhoven) - TBA
14:30 – 15:00: Coffee break
15:00 – 16:00: Invited talks III
TBA
TBA
16:00 – 17:00: Closing and drinks
Martin Benning (UCL) - Mind the Gap - Bilevel Optimisation with Fenchel-Young Functions. Inverse problems are increasingly solved by combining physics-based variational models with data-driven parameter learning, but standard bilevel optimisation remains computationally demanding because each gradient step typically requires differentiating through an inner optimisation problem. This talk presents a Fenchel-Young perspective that replaces the classical bilevel loss with a gap-function objective, interpretable as a generalised duality gap and, under suitable conditions, as a Bregman distance. The resulting formulation preserves the core modelling structure of variational regularisation while yielding substantially simpler gradients, avoiding repeated solutions of adjoint and fixed-point equations. We will outline key theoretical links to constrained formulations (including Morozov- and Ivanov-type regularisation), discuss variational error estimates with convergence guarantees, and show how the framework extends from scalar to spatially varying regularisation parameters and infimal convolution regularisations. Numerical evidence in compressed sensing and imaging settings indicates comparable reconstruction quality to bilevel baselines at markedly lower computational cost. This is joint work with Alexandra Valavanis (Queen Mary University of London) and Clarice Poon (University of Warwick).