847 research outputs found
Simulation of hyperelastic materials in real-time using Deep Learning
The finite element method (FEM) is among the most commonly used numerical
methods for solving engineering problems. Due to its computational cost,
various ideas have been introduced to reduce computation times, such as domain
decomposition, parallel computing, adaptive meshing, and model order reduction.
In this paper we present U-Mesh: a data-driven method based on a U-Net
architecture that approximates the non-linear relation between a contact force
and the displacement field computed by a FEM algorithm. We show that deep
learning, one of the latest machine learning methods based on artificial neural
networks, can enhance computational mechanics through its ability to encode
highly non-linear models in a compact form. Our method is applied to two
benchmark examples: a cantilever beam and an L-shape subject to moving punctual
loads. A comparison between our method and proper orthogonal decomposition
(POD) is done through the paper. The results show that U-Mesh can perform very
fast simulations on various geometries, mesh resolutions and number of input
forces with very small errors
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