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Toutes les publications du projet ERC.

Affichage des publications 21 à 30 sur 48 au total

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 -

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. -

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 -

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) -

Engineering Sciences

Data-based Model Updating, Selection, and Enrichment using the Modified Constitutive Relation Error Concept

In the context of computational engineering, physical systems are classically represented by a mathematical model which can be picked in a hierarchical list of possible models, with increasing complexity, and which is then numerically processed to…

Ludovic Chamoin, Antoine Benady, Sahar Farahbakhsh, Emmanuel Baranger, Martin Poncelet

15th World Congress on Computational Mechanics -