/dem_hyperelasticity

A method based on a feed forward neural network to solve partial differential equations in nonlinear elasticity at finite strain based on the idea of minimum potential energy. The method is named "Deep Energy Method".

Primary LanguagePython

Paper: A deep energy method for finite deformation hyperelasticity

Authors: Vien Minh Nguyen-Thanh, Xiaoying Zhuang, Timon Rabczuk

European Journal of Mechanics - A/Solids Available online 25 October 2019, 103874 https://doi.org/10.1016/j.euromechsol.2019.103874

Contact: ntvminh286@gmail.com (institute email: minh.nguyen@iop.uni-hannover.de)

Setup:

  1. Create DeepEnergyMethod directory: cd <workingdir>; mkdir DeepEnergyMethod

  2. Download dem_hyperelasticity source code and put it under DeepEnergyMethod. The directory is like <workingdir>/DeepEnergyMethod/dem_hyperelasticity

  3. Setup environment with conda: conda create -n demhyper python=3.7

  4. Switch to demhyper environment to start working with dem: source activate demhyper

  5. Install numpy, scipy, matplotlib: pip install numpy scipy matplotlib

  6. Install pytorch and its dependencies: conda install pytorch; conda install pytorch-cpu torchvision-cpu -c pytorch

  7. Install pyevtk for view in Paraview: pip install pyevtk

  8. Setup PYTHONPATH environment by doing either a.(temporary use) or b.(permanent use):

a. export PYTHONPATH="$PYTHONPATH:<workingdir>/DeepEnergyMethod"

b. add the above line to the end of file ~/.bashrc and execute "source ~/.bashrc"

Optional:

To use fem to compare the results with fem, we recommend to install fenics

  1. conda config --add channels conda-forge

  2. !conda install fenics