3 papers
stat.ML2021
Convex regularization in statistical inverse learning problems
Tatiana A. Bubba, Martin Burger, Tapio Helin +1
We consider a statistical inverse learning problem, where the task is to estimate a function based on noisy point evaluations of , where is a linear operator. The funct…
math.OC2020
Deep neural networks for inverse problems with pseudodifferential operators: an application to limited-angle tomography
Tatiana A. Bubba, Mathilde Galinier, Matti Lassas +3
We propose a novel convolutional neural network (CNN), called DONet, designed for learning pseudodifferential operators (DOs) in the context of linear inverse problems. Our s…
math.AP2019
On the determination of ischemic regions in the monodomain model of cardiac electrophysiology from boundary measurements
Elena Beretta, Cecilia Cavaterra, Luca Ratti
In this paper we consider the monodomain model of cardiac electrophysiology. After an analysis of the well-posedness of the model, we determine an asymptotic expansion of the pertu…