Inverse problems arise in practical applications whenever one needs to deduce unknowns
from observables. This monograph is a valuable contribution to the highly topical field of …

In multistatic imaging one uses waves to probe for information about an unknown medium.
These waves can be acoustic, elastic, or electromagnetic. They can be at zero-frequency …

We have developed a wavelet-multiscale adjoint scheme for the elastic full-waveform
inversion of seismic data, including body waves (BWs) and surface waves (SWs). We start …

Elastic full waveform inversion (EFWI) aims to reduce the misfit between recorded and
modelled multicomponent seismic data for deducing a detailed model of elastic parameters …

In this work we consider stochastic gradient descent (SGD) for solving linear inverse
problems in Banach spaces. SGD and its variants have been established as one of the most …

We present a general framework to study uniqueness, stability and reconstruction for infinite-
dimensional inverse problems when only a finite-dimensional approximation of the …

The availability of cancer measurements over time enables the personalized assessment of
tumor growth and therapeutic response dynamics. However, many tumors are treated after …

In this work, we present and study Continuous Generative Neural Networks (CGNNs),
namely, generative models in the continuous setting: the output of a CGNN belongs to an …

In this paper we study the inverse boundary value problem of determining the potential in
the Schrödinger equation from the knowledge of the Dirichlet-to-Neumann map, which is …

Tikhonov regularization is one of the most commonly used methods for the regularization of
ill-posed problems. In the setting of finite element solutions of elliptic partial differential …