86 research outputs found
A Hybrid GNN approach for predicting node data for 3D meshes
Metal forging is used to manufacture dies. We require the best set of input
parameters for the process to be efficient. Currently, we predict the best
parameters using the finite element method by generating simulations for the
different initial conditions, which is a time-consuming process. In this paper,
introduce a hybrid approach that helps in processing and generating new data
simulations using a surrogate graph neural network model based on graph
convolutions, having a cheaper time cost. We also introduce a hybrid approach
that helps in processing and generating new data simulations using the model.
Given a dataset representing meshes, our focus is on the conversion of the
available information into a graph or point cloud structure. This new
representation enables deep learning. The predicted result is similar, with a
low error when compared to that produced using the finite element method. The
new models have outperformed existing PointNet and simple graph neural network
models when applied to produce the simulations
Couplage Global-Local en asynchrone pour des probl\`emes lin\'eaires
An asynchronous parallel version of the non-intrusive global-local coupling
is implemented. The case of many patches, including those covering the entire
structure, is studied. The asynchronism limits the dependency on
communications, failures, and load imbalance. We detail the method and
illustrate its performance in an academic case.Comment: in French languag
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