activity
20192026
most citedNumerical analysis of the LDG method for large deformations of prestrained plates

4 citations · 8 across the 12 of their papers we have counts for

collaborators

16 papers

math.NA2026

A Tree-Based Localized Reduced Basis Method for Parametrized Parabolic PDEs

Mohamed Barakat, Diane Guignard

We propose a localized reduced basis method for parametrized parabolic partial differential equations based on an adaptive partitioning of the parameter domain using hierarchical b…

math.NA2026

A near-optimal recovery algorithm for the Stokes equations with incomplete information on the boundary conditions

Andrea Bonito, Diane Guignard

We address the problem of numerically approximating the velocity and pressure governed by the Stokes system when the boundary conditions are only partially known and thus do not un…

math.NA2026

Error analysis for the approximation of a flow in deformable porous media with nonlinear strain-stress relation

Andrea Bonito, Vivette Girault, Diane Guignard

We study a model describing the slow flow of a fluid through a deformable, porous, elastic solid undergoing small deformations. The stress-strain relationship of the solid incorpor…

math.NA2025

Convergence analysis for a tree-based nonlinear reduced basis method

Mohamed Barakat, Diane Guignard

We develop and analyze a nonlinear reduced basis (RB) method for parametrized elliptic partial differential equations based on a binary-tree partition of the parameter domain into…

math.NA2024

Approximating partial differential equations without boundary conditions

Andrea Bonito, Diane Guignard

We consider the problem of numerically approximating the solutions to an elliptic partial differential equation (PDE) for which the boundary conditions are lacking. To alleviate th…

math.NA2024

Tree-Based Nonlinear Reduced Modeling

Diane Guignard, Olga Mula

This paper is concerned with model order reduction of parametric Partial Differential Equations (PDEs) using tree-based library approximations. Classical approaches are formulated…