Publications

Structural mechanics

Geometry-agnostic model reduction with GNN-generated reduced POD bases and boosted PGD enrichment for (non)linear structural elastodynamics

This contribution proposes a new and significantly enhanced extension of a recently-introduced hybrid Graph Neural Network (GNN)-based reduced-order modeling approach for the numerical solution of time-dependent partial differential equations on non…

Victor Matray, David Néron, Frédéric Feyel, Faisal Amlani

Computer Methods in Applied Mechanics and Engineering - 2026

Engineering Sciences

A two-stage framework combining derivative-free MCMC sampling and transport maps for black-box Bayesian inverse problems

Dawen Wu, Ludovic Chamoin

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering - 2026

Engineering Sciences

On Detecting Elastoplastic Shakedown Using Minimal Digital Image Correlation Results and an Elastic Model: Demonstration for AA6061 Auxetic Sheets

Background. Shakedown occurs in elastoplastic bodies subject to cyclic loading when limited plastic deformation in the early stages of cycling gives rise to residual stresses that arrest the plastic response. As a result, purely elastic behavior is…

Shen Wang, François Hild, Natasha Vermaak

Experimental Mechanics - 2026

Risques

Parameter influence analysis in a 3D TBM model via sensitivity analysis and GP metamodels

Urban tunnel excavation with tunnel boring machines induces ground movements that can affect nearby structures. Three-dimensional finite element models (FEM) are widely used to predict these settlements, but their high computational cost limits…

Boris Kratz, Pierre Jehel, Maxime Tatin, Emmanuel Vazquez

Computers and Geotechnics - 2026

Géotechnique

Reinforcing the Mechanical Strength of Soils Under the Saturated State by Biopolymer Treatment

Min Yao, Benhui Fan, Fernando Lopez‐caballero, Jean-Marie Fleureau

2025 International Conference on Bio-mediated and Bio-inspired Geotechnics (ICBBG) - 2025

Modeling and Simulation

Domain decomposition method for simulating the cracking of cross-laminated timber structures

Technological advances in cross-laminated timber (CLT) have promoted its use in medium- and high-rise buildings due to its excellent strength-to-weight ratio, good seismic performance, and insulating capacity. However, predicting the response of CLT…

Jorge Fernández González

Environmental Sciences

Substance use disorders in outpatients with bipolar disorder and pain complaints

Introduction: Pain and substance use disorders (SUDs) are common in bipolar disorder (BD), yet the associations between these comorbid conditions are poorly understood. Our hypotheses were: (1) the frequency of SUDs may vary according to pain…

J. Chabert, R. Icick, J. Cabé, X. Moisset, O. Godin, S. Gard, B. Aouizerate, J. Loftus, V. Aubin, A. Lefrere, C. Dubertret, P. Roux, M. Polosan, T. Schwitzer, B. Etain, R. Moirand, E. Olié, E. Haffen, M. Leboyer, P. Courtet, M. Walter, N. Bouaziz, A. Yrondi, R. Belzeaux, P.M. Llorca, G. Brousse, L. Samalin

Journal of Affective Disorders - 2025

Vibrations

Modélisation et simulation des mécanismes de diffusion des ultrasons dans les composants WLAM en vue d'une caractérisation multi-échelle

The scattering of elastic waves in polycrystalline materials is a physically interesting phenomenon that is widely exploited in the field of Non-Destructive Testing (NDT), as the Ultrasound-Laser Technique. Polycrystalline materials often exhibit…

Juan-Camilo Victoria-Giraldo

Engineering Sciences

Méthodes de diffusion et apprentissage par score-matching appliqués aux équations de Navier–Stokes stochastiques

Dans le cadre déterministe, les équations de Navier–Stokes incompressibles décrivent les fluides visqueux et sont, en deux dimensions, globalement bien posées sous des hypothèses classiques. Cependant, elles ne capturent pas la variabilité et les…

Wilfried Genuist

GDR - GAIA 2025 - 2025

Risques

Breaking the Black Box: Inherently Interpretable Physics‐Constrained Machine Learning With Weighted Mixed‐Effects for Imbalanced Seismic Data

Abstract Ground motion models (GMMs) are critical for seismic risk mitigation and infrastructure design. Machine learning (ML) is increasingly applied to GMM development due to expanding strong motion databases. However, existing ML‐based GMMs…

Vemula Sreenath, Filippo Gatti, Pierre Jehel

Journal of Geophysical Research. Machine Learning and Computation - 2025