Data related to the publication (we would be grateful if you could cite the paper in the case in which you are using the data) title = "A recurrent neural network-accelerated multi-scale model for elasto-plastic heterogeneous materials subjected to random cyclic and non-proportional loading paths", journal = "Computer Methods in Applied Mechanics and Engineering", pages = " 113234", year = "2020", issn = "0045-7825", doi = "https://doi.org/10.1016/j.cma.2020.113234", author = "Wu, Ling and Nguyen, Van Dung and Kilingar, Nanda Gopala and Noels, Ludovic"This project has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement No 862015 for the project "Multi-scale Optimisation for Additiv...
In a concurrent (FE2) multiscale modeling is an increasingly popular approach for modeling complex m...
Metamaterials are a group of materials with artificial engineered structures that exhibits customize...
International audienceThis paper presents an original link between neural networks theory and mechan...
Data related to the publication (we would be grateful if you could cite the paper in the case in whi...
An artificial Neural Network (NNW) is designed to serve as a surrogate model of micro-scale simulati...
Driven by the need to accelerate numerical simulations, the use of machine learning techniques is ra...
Data for: On the Importance of Self-consistency in Recurrent Neural Network Models Representing Elas...
The present work aims at proposing a hybrid physics-AI based model to predict non-linear mechanical ...
FE2 multiscale simulations of history-dependent materials are accelerated by means of a recurrent ne...
Data related to =========== title = "Recurrent Neural Networks (RNNs) with dimensionality reduction ...
Codes for the conference paper: Title : Fracture Estimation based on Deformation History with Rec...
Recurrent Neural Network (RNN) based surrogate models constitute an emerging class of reduced order ...
peer reviewedArtificial Neural Networks (NNWs) are appealing functions to substitute high dimensiona...
A mechanistically informed data-driven approach is proposed to simulate the complex plastic behavior...
International audienceThis paper is concerned with the micro-physically motivated elasto-visco-plast...
In a concurrent (FE2) multiscale modeling is an increasingly popular approach for modeling complex m...
Metamaterials are a group of materials with artificial engineered structures that exhibits customize...
International audienceThis paper presents an original link between neural networks theory and mechan...
Data related to the publication (we would be grateful if you could cite the paper in the case in whi...
An artificial Neural Network (NNW) is designed to serve as a surrogate model of micro-scale simulati...
Driven by the need to accelerate numerical simulations, the use of machine learning techniques is ra...
Data for: On the Importance of Self-consistency in Recurrent Neural Network Models Representing Elas...
The present work aims at proposing a hybrid physics-AI based model to predict non-linear mechanical ...
FE2 multiscale simulations of history-dependent materials are accelerated by means of a recurrent ne...
Data related to =========== title = "Recurrent Neural Networks (RNNs) with dimensionality reduction ...
Codes for the conference paper: Title : Fracture Estimation based on Deformation History with Rec...
Recurrent Neural Network (RNN) based surrogate models constitute an emerging class of reduced order ...
peer reviewedArtificial Neural Networks (NNWs) are appealing functions to substitute high dimensiona...
A mechanistically informed data-driven approach is proposed to simulate the complex plastic behavior...
International audienceThis paper is concerned with the micro-physically motivated elasto-visco-plast...
In a concurrent (FE2) multiscale modeling is an increasingly popular approach for modeling complex m...
Metamaterials are a group of materials with artificial engineered structures that exhibits customize...
International audienceThis paper presents an original link between neural networks theory and mechan...