collaborators

8 papers

cs.CE2026

Spectral Filtering of 3D Integral Operators Using Modified Green's Functions

Alessandro Bellusci, Viviana Giunzioni, Adrien Merlini +1

Several recent contributions have analyzed and illustrated the effectiveness of operator filtering, both in terms of regularization and compression, when handling dense matrices ar…

cs.CE2026

A Numerical Approach to Operator Filtering within the Adaptive Integral Method for Electromagnetic Integral Equations

Tommaso Pignatelli, Viviana Giunzioni, Paolo Ricci +3

Operator filtering allows for the regularization and compression of dense integral operators, effectively mitigating the memory and computational costs associated with iterative so…

cs.CE2026

High-Frequency Preconditioners for Electromagnetic Integral Equations Based on Helmholtz Regularizations

S. Ciciriello, V. Giunzioni, A. Dély +3

The numerical solution of the Electric Field Integral Equation (EFIE) via the Boundary Element Method (BEM) can be computationally challenging due to conditioning issues arising in…

eess.SP2026

Majorization-Minimization Networks for Inverse Problems: An Application to EEG Imaging

Le Minh Triet Tran, Sarah Reynaud, Ronan Fablet +3

Inverse problems are often ill-posed and require optimization schemes with strong stability and convergence guarantees. While learning-based approaches such as deep unrolling and m…

math.NA2026

Asymptotic Spectral Insights Behind Fast Direct Solvers for High-Frequency Electromagnetic Integral Equations on Non-Canonical Geometries

V. Giunzioni, C. Henry, A. Merlini +1

Integral-equation-based fast direct solvers for electromagnetic scattering can substantially reduce computational costs, especially in the presence of multiple excitations. We rece…

math.NA2025

A High-Order Discretization Scheme for Surface Integral Equations for Analyzing the Electroencephalography Forward Problem

Rui Chen, Viviana Giunzioni, Adrien Merlini +1

A Nystrom-based high-order (HO) discretization scheme for surface integral equations (SIEs) for analyzing the electroencephalography (EEG) forward problem is proposed in this work.…