51 citations · 388 across the 44 of their papers we have counts for
46 papers
An Efficient Machine Learning Approach for Degradation Forecasting in AEM Water Electrolysis
Marco Veneriano, Ani Gjergji, Sebastiano Bellani +3
This study provides a data-driven analysis of a novel dataset of single-cell Anion Exchange Membrane water electrolyzers (AEMWE), operated under constant current load across multip…
Analytic inverse problems with finitely many random measurements
Giovanni S. Alberti, Damiano Poletti, Simone Sanna +1
While infinite-dimensional inverse problems are traditionally analyzed assuming continuous data, practical applications rely on finitely many discrete measurements. Recent determin…
PIKS: Universal Physics-Informed Kernel Methods
Joachim Bona-Pellissier, Giacomo Meanti, Matteo Santacesaria +1
Physics-informed machine learning incorporates physical principles --often expressed via differential operators-- into data-driven models. While physics-informed neural networks (P…
Stochastic Generalized Sampling
Luca Finotti, Matteo Santacesaria
Reconstructing an infinite-dimensional signal from a finite set of measurements is a fundamental problem in approximation theory and signal processing. While the generalized sampli…
Diffusion Graph Posterior Sampling for Nonlinear Inverse Problems with Application to Electrical Impedance Tomography
Giovanni S. Alberti, Damiana Lazzaro, Serena Morigi +2
Deep generative models have emerged as state-of-the-art for solving inverse problems, but applying them to inverse problems for PDEs, like electrical impedance tomography (EIT) rem…
Infinite dimensional generative sensing
Paolo Angella, Vito Paolo Pastore, Matteo Santacesaria
Deep generative models have become a standard for modeling priors for inverse problems, going beyond classical sparsity-based methods. However, existing theoretical guarantees are…
Inverse problems for quasi-linear elliptic systems modeling electrolysers
Giovanni S. Alberti, Wadim Gerner, Matteo Santacesaria
We investigate the electrochemical processes within an electrolyser cell, which are modelled by a coupled system of second-order quasi-linear elliptic PDEs. In this context, we stu…
MAD: Manifold Attracted Diffusion
Dennis Elbrächter, Giovanni S. Alberti, Matteo Santacesaria
Score-based diffusion models are a highly effective method for generating samples from a distribution of images. We consider scenarios where the training data comes from a noisy ve…
Deep Unfolding Network for Nonlinear Multi-Frequency Electrical Impedance Tomography
Giovanni S. Alberti, Damiana Lazzaro, Serena Morigi +2
Multi-frequency Electrical Impedance Tomography (mfEIT) represents a promising biomedical imaging modality that enables the estimation of tissue conductivities across a range of fr…
On spatial systems of cities
Gianandrea Lanzara, Matteo Santacesaria
Are there multiple equilibria in the spatial economy? This paper develops a unified framework that integrates systems of cities and regional models to address this question within…
DIMA: DIffusing Motion Artifacts for unsupervised correction in brain MRI images
Paolo Angella, Luca Balbi, Fabrizio Ferrando +4
Motion artifacts remain a significant challenge in Magnetic Resonance Imaging (MRI), compromising diagnostic quality and potentially leading to misdiagnosis or repeated scans. Exis…
TomoSelfDEQ: Self-Supervised Deep Equilibrium Learning for Sparse-Angle CT Reconstruction
Tatiana A. Bubba, Matteo Santacesaria, Andrea Sebastiani
Deep learning has emerged as a powerful tool for solving inverse problems in imaging, including computed tomography (CT). However, most approaches require paired training data with…
Assessing the use of Diffusion models for motion artifact correction in brain MRI
Paolo Angella, Vito Paolo Pastore, Matteo Santacesaria
Magnetic Resonance Imaging generally requires long exposure times, while being sensitive to patient motion, resulting in artifacts in the acquired images, which may hinder their di…
Compressed sensing for inverse problems II: applications to deconvolution, source recovery, and MRI
Giovanni S. Alberti, Alessandro Felisi, Matteo Santacesaria +1
This paper extends the sample complexity theory for ill-posed inverse problems developed in a recent work by the authors [`Compressed sensing for inverse problems and the sample co…
Learning sparsity-promoting regularizers for linear inverse problems
Giovanni S. Alberti, Ernesto De Vito, Tapio Helin +3
This paper introduces a novel approach to learning sparsity-promoting regularizers for solving linear inverse problems. We develop a bilevel optimization framework to select an opt…
Is in-domain data beneficial in transfer learning for landmarks detection in x-ray images?
Roberto Di Via, Matteo Santacesaria, Francesca Odone +1
In recent years, deep learning has emerged as a promising technique for medical image analysis. However, this application domain is likely to suffer from a limited availability of…
Localization of point scatterers via sparse optimization on measures
Giovanni S. Alberti, Romain Petit, Matteo Santacesaria
We consider the inverse scattering problem for time-harmonic acoustic waves in a medium with pointwise inhomogeneities. In the Foldy-Lax model, the estimation of the scatterers' lo…
Learning a Gaussian Mixture for Sparsity Regularization in Inverse Problems
Giovanni S. Alberti, Luca Ratti, Matteo Santacesaria +1
In inverse problems, it is widely recognized that the incorporation of a sparsity prior yields a regularization effect on the solution. This approach is grounded on the a priori as…
Manifold Learning by Mixture Models of VAEs for Inverse Problems
Giovanni S. Alberti, Johannes Hertrich, Matteo Santacesaria +1
Representing a manifold of very high-dimensional data with generative models has been shown to be computationally efficient in practice. However, this requires that the data manifo…
Compressed sensing for inverse problems and the sample complexity of the sparse Radon transform
Giovanni S. Alberti, Alessandro Felisi, Matteo Santacesaria +1
Compressed sensing allows for the recovery of sparse signals from few measurements, whose number is proportional to the sparsity of the unknown signal, up to logarithmic factors. T…
Continuous Generative Neural Networks: A Wavelet-Based Architecture in Function Spaces
Giovanni S. Alberti, Matteo Santacesaria, Silvia Sciutto
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 infinite-d…
Market Areas in General Equilibrium
Gianandrea Lanzara, Matteo Santacesaria
This paper proposes a spatial model with a realistic geography where a continuous distribution of agents (e.g., farmers) engages in economic interactions with one location from a f…
Learning the optimal Tikhonov regularizer for inverse problems
Giovanni S. Alberti, Ernesto De Vito, Matti Lassas +2
In this work, we consider the linear inverse problem , where is a known linear operator between the separable Hilbert spaces and , is a random v…
Neural networks for classification of strokes in electrical impedance tomography on a 3D head model
Valentina Candiani, Matteo Santacesaria
We consider the problem of the detection of brain hemorrhages from three dimensional (3D) electrical impedance tomography (EIT) measurements. This is a condition requiring urgent t…