activity
20172026
most citedAccelerating the convergence of Newton's method for nonlinear elliptic PDEs using Fourier neural operators

11 citations · 12 across the 5 of their papers we have counts for

collaborators

7 papers

math.NA2026

Asymptotic gauge-invariant Hybrid High-Order method for magnetic Schrödinger equations

Joubine Aghili

We introduce a Hybrid High-Order (HHO) method for the Schrödinger equation in the presence of a magnetic vector potential. In quantum mechanics, physical observables are invariant…

math.NA2026

Data-efficient extraction of optical properties from 3D Monte Carlo TPSFs using Bi-LSTM transfer learning

Joubine Aghili, Rémi Imbach, Anne Pallarès +2

Time-Resolved Spectroscopy (TRS) is a powerful modality for non-invasive characterization of turbid media. However, extracting optical properties, absorption and reduced scat…

math.NA2026

Nonlinear compressive reduced basis approximation : when Taylor meets Kolmogorov

Joubine Aghili, Hassan Ballout, Yvon Maday +1

This paper investigates model reduction methods for efficiently approximating the solution of parameter-dependent PDEs with a multi-parameter vector . In ca…

math.NA2024★ 11 cited

Accelerating the convergence of Newton's method for nonlinear elliptic PDEs using Fourier neural operators

Joubine Aghili, Emmanuel Franck, Romain Hild +2

It is well known that Newton's method can have trouble converging if the initial guess is too far from the solution. Such a problem particularly occurs when this method is used to…

math.NA2024

A Dynamical Neural Galerkin Scheme for Filtering Problems

Joubine Aghili, Joy Zialesi Atokple, Marie Billaud-Friess +3

This paper considers the filtering problem which consists in reconstructing the state of a dynamical system with partial observations coming from sensor measurements, and the knowl…

math.OC2020★ 1 cited

Depth-Adaptive Neural Networks from the Optimal Control viewpoint

Joubine Aghili, Olga Mula

In recent years, deep learning has been connected with optimal control as a way to define a notion of a continuous underlying learning problem. In this view, neural networks can be…

math.NA2017

An advection-robust Hybrid High-Order method for the Oseen problem

Joubine Aghili, Daniele A. Di Pietro

In this work, we study advection-robust Hybrid High-Order discretizations of the Oseen equations. For a given integer , the discrete velocity unknowns are vector-valued pol…