PINN_Comp_Mech

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PINN program for computational mechanics

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  - Post_Process.py
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- Description.md
- Main.py
- - Description.md
  - Post_Process.py
  - Pre_Process.py
  - Train.py
- Description.md
- Main.py
- - Description.md
  - Post_Process.py
  - Pre_Process.py
  - Train.py
- Description.md
- Main.py
- - Description.md
  - Post_Process.py
  - Pre_Process.py
  - Train.py
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README.md

// repository documentation

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# Physics-informed neural network-based computational solid mechanics
This repository provides numerical examples of physics-informed neural network-based computational solid mechanics framework.

Physics-informed neural network (PINN) has recently gained increasing interest in computational  mechanics.

This work aims to help the researchers who are interested in the PINN-based solid mechanics solver to have a clear insight into this emerging area.

Numerical examples include:
 - 1D stretching plate problem (implemented by collocation and energy-based loss functions)
 - 2D in-plain stretching plate problem (implemented by collocation loss function)
 - 2D in-plain stretching plate problem (implemented by energy-based loss function)
 - 3D streching cube problem (implemented by collocation loss function)

Information regarding the numerical examples, please refer to our paper.

# Enviornmental settings
 - TensorFlow  2.8.0 
 - Keras       2.8.0
 - SciPy       1.8.0
 - Matplotlib  3.6.0
 
# Paper link
Now, the paper has been accepted by the ***International Journal of Computational Methods***(IJCM):  
https://doi.org/10.1142/S0219876223500135  
Besides, the preprint version can be found at:  
https://arxiv.org/abs/2210.09060

# Cite as
[1] J. Bai, H. Jeong, C.P. Batuwatta-Gamage, S. Xiao, Q. Wang, C.M. Rathnayaka, L. Alzubaidi, G.-R. Liu, Y. Gu, An introduction to programming Physics-Informed Neural Network-based computational solid mechanics, International Journal of Computational Methods, (2023), DOI: 10.1142/S0219876223500135.  
or  
[1] J. Bai, H. Jeong, C.P. Batuwatta-Gamage, S. Xiao, Q. Wang, C.M. Rathnayaka, L. Alzubaidi, G.-R. Liu, Y. Gu, An introduction to programming Physics-Informed Neural Network-based computational solid mechanics, Arxiv preprint  arXiv:2210.09060, (2022).

# Contact us
For questions regarding the code, please contact:

jinshuai.bai@hdr.qut.edu.au

yuantong.gu@qut.edu.au

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