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This repository serves as a comprehensive tutorial on introducing PINNs to solve elasticity problems. It demonstrates how PINNs can predict stress distribution solely based on the physical laws of elasticity theory.

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ElasticityPINN

ElasticityPINN is a ...

Table of Contents

Overview

ElasticityPINN was developed to ...

Usage

To get started with ElasticityPINN, follow these steps:

  1. Clone the repository:
    git clone https://github.com/estevaofuzaro98/ElasticityPINN.git
  2. Navigate to the package directory:
    cd ElasticityPINN

Documentation

The routines in ElasticityPINN are well-commented to explain their functionality. The main routine was coded in Python Notebook within useful comments along the cells.......

Authors

  • Estevão Fuzaro de Almeida
  • Samuel da Silva

Citing ElasticityPINN

If you use ElasticityPINN in your research, please cite the following publication:

  • E. F. Almeida, S. Silva, Some Regards on using Physics-Informed Neural Networks for Solving Two-Dimensional Elasticity Problems, Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2025 http://dx.doi.org/10.000/doi2bedefined
@article{Almeida2025,
   author       = {E. F. Almeida and S. Silva},
   title        = {Some Regards on using Physics-Informed Neural Networks for Solving Two-Dimensional Elasticity Problems},
   year         = {2025},
   journal      = {Journal of the Brazilian Society of Mechanical Sciences and Engineering},
   volume.      = {XX},
   pages        = {XXXXX},
   note         = {10.000/doi2bedefined},
}

License

ElasticityPINN is released under the MIT license. See the LICENSE file for details. All new contributions must be made under the MIT license.

Institutional support

   

Funding

  • São Paulo Research Foundation (FAPESP), grant number 2022/16156-9
  • National Council for Scientific and Technological Development (CNPq/Brazil), grant number 309467/2023-3
  • National Institute of Science and Technology, Smart Structures in Engineering (INCT-EIE)
    • Funded by the Brazilian agencies:
      • CNPq, grant number 406148/2022-8
      • Coordination for the Improvement of Higher Education Personnel (CAPES)
      • Minas Gerais State Research Support Foundation (FAPEMIG)

Contact

For any questions or further information, please contact the authors at:

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This repository serves as a comprehensive tutorial on introducing PINNs to solve elasticity problems. It demonstrates how PINNs can predict stress distribution solely based on the physical laws of elasticity theory.

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