6 citations · 12 across the 5 of their papers we have counts for
3 papers · 1 filter
An efficient Quasi-Newton method for nonlinear inverse problems via learned singular values
Danny Smyl, Tyler N. Tallman, Dong Liu +1
Solving complex optimization problems in engineering and the physical sciences requires repetitive computation of multi-dimensional function derivatives. Commonly, this requires co…
Joint Reconstruction and Low-Rank Decomposition for Dynamic Inverse Problems
Simon Arridge, Pascal Fernsel, Andreas Hauptmann
A primary interest in dynamic inverse problems is to identify the underlying temporal behaviour of the system from outside measurements. In this work we consider the case, where th…
On Learned Operator Correction in Inverse Problems
Sebastian Lunz, Andreas Hauptmann, Tanja Tarvainen +2
We discuss the possibility to learn a data-driven explicit model correction for inverse problems and whether such a model correction can be used within a variational framework to o…