Thermodynamically consistent Recurrent Neural Networks to predict non linear behaviors of dissipative materials subjected to non-proportional loading paths
A Danoun, E Prulière, Y Chemisky - Mechanics of Materials, 2022 - Elsevier
The present work aims at proposing a hybrid physics-AI based model to predict non-linear
mechanical behaviors of dissipative materials. By introducing a specific Neural Network …
mechanical behaviors of dissipative materials. By introducing a specific Neural Network …
[HTML][HTML] FE-LSTM: A hybrid approach to accelerate multiscale simulations of architectured materials using Recurrent Neural Networks and Finite Element Analysis
A Danoun, E Prulière, Y Chemisky - Computer Methods in Applied …, 2024 - Elsevier
In the present work, a novel modeling strategy to accelerate multi-scale simulations of
heterogeneous materials using deep neural networks is developed. This approach, called …
heterogeneous materials using deep neural networks is developed. This approach, called …
[PDF][PDF] Classification et estimation de densité de microstructures triplement périodiques avec des réseaux de neurones à convolution 3D
MRG Garban, Y Chemisky, E Prulière… - 16ème Colloque National …, 2024 - hal.science
Le présent travail vise à proposer une contribution méthodologique pour l'identification et la
prédiction de densité de microstructures en utilisant un réseau de neurones convolutif à trois …
prédiction de densité de microstructures en utilisant un réseau de neurones convolutif à trois …