Publications

Engineering Sciences

A Data-Driven Tube Model Predictive Control Framework with Recursive Feasibility and Stability Guarantees

The use of deep neural networks as fast approximators of optimal control laws is explored in nonlinear Model Predictive Control, primarily for their ability to reduce online computational complexity. Despite their practical advantages, integrating…

Sivalingam Mahalingam, Ludovic Chamoin

17th World Congress on Computational Mechanics (WCCM 2026) - 2026

Engineering Sciences

Validation criteria for data-driven models of history-dependent materials

Ludovic Chamoin, Pierre Ladevèze

17th World Congress on Computational Mechanics (WCCM 2026) - 2026

Engineering Sciences

Contrôle santé intégré des structures basée sur l’assimilation de données en temps réel et les jumeaux numériques

Ludovic Chamoin

Séminaire EDF - 2026

Engineering Sciences

Integrated Structural Health Monitoring with Real-time Data Assimilation and Hybrid Twins of Dynamical Systems

Ludovic Chamoin

Séminaire de l'Institut de Mathématiques de Bordeaux (IMB) - 2026

Engineering Sciences

Continuous structural health monitoring with a modified dual Kalman filter applied with optical fiber sensing data

The advent of new sensing technologies has led to an increased use of sensors for the monitoring of systems and structures. By integrating data from these sensors with numerical models, a customized digital twin of the struc ture can be constructed…

S. Farahbakhsh, Ludovic Chamoin, M. Poncelet

Computers & Structures - 2026

Engineering Sciences

MPC surrogates based on supervised or unsupervised learning for the real-time control of nonlinear systems

While Model Predictive Control (MPC) is a widely used method for controlling constrained nonlinear systems, data-driven MPC has recently emerged as a viable alternative to explicit MPC when sufficient data is available. This work investigates two…

Daniel Martin Xavier, Ludovic Chamoin, Laurent Fribourg

IFAC Journal of Systems and Control - 2026

Engineering Sciences

From data to reality: Criteria for the validation of data-driven models for history-dependent materials

This paper addresses the issue of validating identified data-driven material models for history-dependent materials, which are nowadays typically represented using neural networks. For this purpose, we introduce an a posteriori general acceptability…

Pierre Ladevèze, Ludovic Chamoin

European Journal of Mechanics - A/Solids - 2026

Engineering Sciences

Effective reduced-order approximation for fast and robust MCRE-based parametric identification of nonlinear history-dependent material laws

This article essentially addresses the numerical frugality of model updating procedures using reduced order modelling. Nonlinear material behaviour is tackled in the article with the focus being on elasto(visco)-plasticity and elasto(visco)-plastic…

Mainak Bhattacharyya, Ludovic Chamoin

Computational Mechanics - 2026

Engineering Sciences

Noise-bias compensation for the unsupervised learning of constitutive laws

The Efficient Unsupervised Constitutive Law Identification and Discovery (EUCLID) framework allows the non-supervised learning of constitutive laws from full-field displacement data and global reaction forces. Nonetheless, its accuracy is adversely…

Clément Jailin, Stéphane Roux, Antoine Benady, Emmanuel Baranger

Comptes Rendus. Mécanique - 2026

Numerical Analysis

A PGD-based reduced order approximation for nonlinear parametric identification using the modified constitutive relation error method and modified Kalman filtering

Mainak Bhattacharyya, Ludovic Chamoin

12th International Conference on Adaptive Modeling and Simulation - 2025