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Simulation of hyperelastic materials in real-time using deep learning.

  • Mendizabal A Inria, Strasbourg, France; University of Strasbourg, ICube, Strasbourg, France. Electronic address: andrea.mendizabal@inria.fr.
  • Márquez-Neila P ARTORG Center, University of Bern, Switzerland.
  • Cotin S Inria, Strasbourg, France. Electronic address: stephane.cotin@inria.fr.
  • 2019-11-10
Published in:
  • Medical image analysis. - 2020
Deep neural networks
Finite element method
Hyperelasticity
Physics-based simulation
Real-time simulation
Reduced order model
Biomechanical Phenomena
Computer Simulation
Datasets as Topic
Deep Learning
Elastic Modulus
Finite Element Analysis
Humans
Liver
Tomography, X-Ray Computed
English 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 three benchmark examples: a cantilever beam, an L-shape and a liver model 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 and topologies, mesh resolutions and number of input forces with very small errors.
Language
  • English
Open access status
green
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Persistent URL
https://sonar.ch/global/documents/233746
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