Over the last two decades, anomalous diffusion processes in which the mean squares
variance grows slower or faster than that in a Gaussian process have found many …

Deep neural network approaches to inverse imaging problems have produced impressive
results in the last few years. In this survey paper, we consider the use of generative models …

Mathematical models of the most physical phenomena are governed by initial and boundary
value problems for partial differential equations (PDEs). Inverse problems governed by …

Since their initial introduction, score-based diffusion models (SDMs) have been successfully
applied to solve a variety of linear inverse problems in finite-dimensional vector spaces due …

Continuous glucose monitoring systems (CGMSs) allow measuring the blood glycaemic
value of a diabetic patient at a high sampling rate, producing a considerable amount of data …

We study an inverse problem of recovering a spatially varying potential term in a one-
dimensional time-fractional diffusion equation from the flux measurements taken at a single …

In this work, we present a deep-learning-based low-frequency (LF) data prediction scheme
to solve the highly nonlinear inverse scattering problem (ISP) with strong scatterers. The …

Over the past few years, deep learning has risen to the foreground as a topic of massive
interest, mainly as a result of successes obtained in solving large-scale image processing …

In this paper, we study nonparametric estimation of instrumental variable (IV) regressions.
Recently, many flexible machine learning methods have been developed for instrumental …

In this study, we introduce a radial basis function neural network (RBFNN) algorithm. The
proposed architecture is employed to solve the inverse Cauchy problems of the Laplace …