Publications

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

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

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

Engineering Sciences

A modified Constitutive Relation Error (mCRE) framework to learn nonlinear constitutive models from strain measurements with thermodynamics-consistent Neural Networks

The damage of mechanical structures is a permanent concern in engineering, related to issues of durability and safety. The theme is currently the subject of various research activities; a typical example is the ERC project DREAM-ON (2021-2026), in…

Antoine Benady, Emmanuel Baranger, Ludovic Chamoin

XI Conference on Adaptive Modeling and Simulation - 2023

Engineering Sciences

A modified Constitutive Relation Error framework to learn nonlinear constitutive laws using physics-augmented Neural Networks

A novel data-driven approach to learn constitutive law with observable data is proposed in the context of structural health monitoring (SHM). Constitutive laws are represented by a physics-augmented neural network taking strain in input and free…

Antoine Benady, Ludovic Chamoin, Emmanuel Baranger

IACM Mechanistic Machine Learning and Digital Engineering for Computational Science Engineering and Technology. - 2023

Engineering Sciences

Model updating with a Modified Dual Kalman Filter acting on distributed strain measurements

Following the advances in measurement technology and its vast availability, mechanical systems and structures are increasingly equipped with sensors to obtain continuous information regarding the system state. Coupled with robust numerical models,…

Sahar Farahbakhsh, Ludovic Chamoin, Martin Poncelet

XI International Conference on Adaptive Modeling and Simulation (ADMOS 2023) - 2023