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Open Source Software and Data 

In order to facilitate collaborations, enable third-party validation and in some cases, fulfill requirements of sponsors, we have provided a number of open source software and data. Unless specified otherwise, those software and data are protected by the Creative Commons Attribution 4.0 International License. Under this license, users must give appropriate credit and indicate if changes are made and are not allowed to apply legal terms and technological measures that legally restrict others from doing anything the license permits. Users must also acknowledge that they are using the software and data at their own risks. This page will be updated periodically. 
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Open Source tutorials on training constitutive laws for MMLDT-CSET conference 

Tutorials

Tutorial 1:  Training neural network constitutive laws. Part I:  supervised learning, physics constraints and validation

Tutorial 2: Training neural network constitutive laws. Part II: PyTorch vs. Tensorflow

Tutorial 3: Training neural network constitutive laws. Part III: Incorporating microstructures with geometric learning

Tutorial 4: Deep reinforcement learning for knowledge graph of plasticity models

Lab Session: Generating constitutive laws from sub-scale DNS simulations

Supplement Materials

Jupyter Notebook
(Tensorflow)


Jupyter Notebook 
(PyTorch)

Jupyter Notebook 
(PyTorch Geometric)


Jupyter Notebook 
(Collaboratory)
Slides

Lecture










Lecture
Lecture Videos

​Video Tutorial, Lecture


​
Video Tutorial



Video Tutorial


​
Lecture


Lab Session

Data-driven causal discovery and uncertainty propagations for constitutive laws

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Repository: 
https://github.com/bbhm-90/MultiGraphRNN 
https://github.com/YanxunXu/MaterialLawCausal 

​Related publications/suggested citations:
  • X. Sun, B. Bahmani, N. Vlassis, W.C. Sun, Y. Xu, Data-driven discovery of interpretable causal relations for deep learning material laws with uncertainty quantification, Granular Matter, accepted, 2021. [arxiv]

​Last Updated: 1/1/2021


​Implementation of phase field fracture model for micro-polar continua 

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Repository: 
https://github.com/hyoungsuksuh/micropolar_phasefield

​Related publications/suggested citations:
  • H.S. Suh, W.C. Sun, An open source FEniCS implementation of a phase field fracture model for micropolar continua, International Journal of Multiscale Computational Engineering, doi:10.1615/IntJMultCompEng.2020033422, 2019. 
  • H.S. Suh, D. O'Conner, W.C. Sun, A phase field model for cohesive fracture in micropolar continua, Computer Methods in Applied Mechanics and Engineering, doi:10.1016/j.cma.2020.113181, 2020.

​Last Updated: 1/1/2020


Discrete element simulation data for training traction-separation law

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Repository (data):
https://data.mendeley.com/datasets/n5v7hyny8n/1

Repository (software):
​See above (Tutorial 4)

​Related publications/suggested citations:
  • K. Wang, W.C. Sun, Meta-modeling game for deriving theory-consistent, micro-structure-based traction-separation laws via deep reinforcement learning, Computer Methods in Applied Mechanics and Engineering, 346:216-241,  doi:10.1016/j.cma.2018.11.026, 2019. [PDF][Bibtex]​
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​Last Updated: 9/1/2019


MATLAB code for 1D poromechanics problems

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Repository: (source code)
https://bit.ly/3duTsn9

​Related publications/suggested citations:
  • W.C. Sun, J.T. Ostien, A.G. Salinger, A stabilized assumed deformation gradient finite element formulation for strongly coupled poromechanical simulations at finite strain, International Journal for Numerical and Analytical Methods in Geomechanics, 37(16):2755-2788, doi:10.1002/nag.2161, 2013. [PDF] [Bibtex]
  • W.C. Sun, Q. Chen, J.T. Ostien, Modeling hydro-mechanical responses of strip and circular footings on saturated collapsible geomaterials, Acta Geotechnica, 9(5):903-934,  doi:10.1007/s11440-013-0276-x, 2014. [PDF] [Bibtex]
  • W.C. Sun, A stabilized finite element formulation for monolithic thermo-hydro-mechanical simulations at finite strain, International Journal for Numerical Methods in Engineering, 103(11):798-839, doi:10.1002/nme.4910, 2015. [PDF] [Bibtex] (This paper is one of the 5 most cited papers from 2015 to 2016 in IJNME, and is selected for the Zienkiewicz Numerical Methods in Engineering Prize [URL #1][URL #2].

​Last Updated: 9/1/2019


Disclaimer: Electronic copies of the articles are provided for convenience only. Before downloading, please check to make sure you have the right permission to download them from your institution. Unless specified otherwise, all materials are subjected to the Attribution 4.0 International (CC BY 4.0) license. If you use/incorporate/reproduce any derivation, data, idea or information from this archive in your own work, you are obligated to give appropriate credit and indicate if changes were made. We appreciate any comment, suggestion or critique, please contact wsun@columbia.edu.  ​
Contact Information
Prof. Steve Sun
Phone: 212-851-4371 
Fax: +1 212-854-6267
Email: wsun@columbia.edu
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