Publications

Artificial Intelligence

Physics-Augmented Neural Networks for Constitutive Modeling: Toward an Application for Structural Health Monitoring

Structural health monitoring is a major concern in the field of engineering [1]. On-board sensing techniques, such as fiber optics, enable accurate in-situ measurements of mechan- ical strain, providing rich experimental data that can be used in…

Antoine Benady, Emmanuel Baranger, Ludovic Chamoin

The 9th European Congress on Computational Methods in Applied Sciences and Engineering - 2024

Neural and Evolutionary Computing

NN-mCRE: a modified Constitutive Relation Error framework for unsupervised learning of nonlinear state laws with physics-augmented Neural Networks

This article proposes a new approach to train physics-augmented neural networks with observable data to represent mechanical constitutive laws. To train the neural network, the proposed method does not rely on strain-stress or strain-free energy…

Antoine Benady, Emmanuel Baranger, Ludovic Chamoin

Engineering Sciences

Real-time monitoring of evolutive structural damage from advanced sensing and simulation

In a digital world with the expansion of connected systems, a still open question is to design smart and autonomous mechanical structures able to perform online control of their health and take anticipated actions during service before downtime of…

Ludovic Chamoin

Colloque IUF 2023 - Evolution - 2023

Engineering Sciences

Scientific machine learning and physics-augmented neural networks for hybrid digital twins

Antoine Benady, Fanny Lehmann, Afsal Pulikkathodi, Emmanuel Baranger, Ludovic Chamoin, Filippo Gatti, Didier Clouteau

Journée du GDR I-GAIA - 2023

Engineering Sciences

A novel DDDAS architecture combining advanced sensing and simulation technologies for effective real-time structural health monitoring

A still open question is how to design smart and autonomous mechanical structures able to perform online monitoring of their integrity and take anticipated actions during service before downtime of failure occur. To address this question, we present…

Ludovic Chamoin, Emmanuel Baranger, Antoine Benady, Pierre-Étienne Charbonnel, Matthieu Diaz, Sahar Farahbakhsh, Laurent Fribourg, Daniel Martin Xavier, Martin Poncelet

Engineering Sciences

In situ structural damage tracking and monitoring from advanced sensing techniques and hybrid twins (distinguished lecture)

Ludovic Chamoin

6th International Conference on Multiscale Computational Methods for Solids and Fluids – MSF 2023 - 2023

Engineering Sciences

Assimilation de données et jumeaux hybrides pour le contrôle santé des structures

Ludovic Chamoin

Journée du Club des Affiliés du LMPS - 2023

Artificial Intelligence

Physics-augmented neural networks for constitutive modeling: training with the modified Constitutive Relation Error

In the context of structural health monitoring (SHM), a novel approach is presented for learning con- stitutive laws using observable data. The proposed method utilizes a data-driven approach, employing a physics-augmented neural network to…

Antoine Benady, Emmanuel Baranger, Ludovic Chamoin

MORTech 2023 – 6th International Workshop on Model Reduction Techniques - 2023

Engineering Sciences

Training and Generalization Errors for Underparameterized Neural Networks

Daniel Martin Xavier, Ludovic Chamoin, Laurent Fribourg

IEEE Control Systems Letters - 2023

Engineering Sciences

Use of physics-augmented neural networks for unsupervised learning of material constitutive relations - Comparison of the NN-Euclid and NN-mCRE methods

Edgar Zembra, Antoine Benady, Emmanuel Baranger, Ludovic Chamoin