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primme

The repository for the Physics-Regulated Interpretable Machine Learning Microstructure Evolution (PRIMME) framework for learning and emulating microstructure grain growth.

https://github.com/eagg-uf/primme

Science Score: 57.0%

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Keywords

artificial-intelligence grain-growth materials-science microstructure neural-network reinforcement-learning transfer-learning
Last synced: 6 months ago · JSON representation ·

Repository

The repository for the Physics-Regulated Interpretable Machine Learning Microstructure Evolution (PRIMME) framework for learning and emulating microstructure grain growth.

Basic Info
  • Host: GitHub
  • Owner: EAGG-UF
  • License: other
  • Language: Jupyter Notebook
  • Default Branch: main
  • Homepage:
  • Size: 116 MB
Statistics
  • Stars: 11
  • Watchers: 2
  • Forks: 8
  • Open Issues: 0
  • Releases: 0
Topics
artificial-intelligence grain-growth materials-science microstructure neural-network reinforcement-learning transfer-learning
Created about 4 years ago · Last pushed 11 months ago
Metadata Files
Readme License Citation

README.md

Physics-Regulated Interpretable Machine Learning Microstructure Evolution (PRIMME)

Description:

Physics-Regularized Interpretable Machine Learning Microstructure Evolution (PRIMME): This code can be used to train and validate PRIMME neural network models for simulating isotropic microstructural grain growth.

To Access the sample Training Dataset from SPPARKS, you can download it from here, it should be placed inside of the /.PRIMME/data directory.

Paper on the Model

Usage

There are two ways to run the program:

Google Colab

See the following Colab link to run PRIMME remotely

Open in Colab

Local GUI

Dependencies

  • Python 3.9 - 3.12
  • For rest, see requirements.txt

Installation

Clone this repository and create virtual environment:

```bash pip install virtualenv # if not done so already

git clone https://github.com/EAGG-UF/PRIMME.git cd PRIMME python3.9 -m venv venv source venv/bin/activate pip install -r requirements.txt

```

Run the GUI Application for Training and Running PRIMME

python cd PRIMME python gui_application.py

Visuals

Isotropic Case

         

Training on mode filter(left), Training on MCP(mid) and Training on phase field (right).

Anisotropic Case

    

Training on mode filter(left) and Training on phase field (right).

Notes:

  • This model is often trained of SPPARKS data, see its GitHub and Documentation for more information.

Contributors:

Weishi Yan, Joel Harley, Joseph Melville, Kristien Everett, Tian Zhihui, Lin Yang, Vishal Yadav, Michael Tonks, Amanda Krause, Gabriel Castejon, Manas Adepu.

Affiliation:

  1. University of Florida, SmartDATA Lab, Department of Electrical and Computer Engineering
  2. University of Florida, Tonks Research Group, Department of Materials Science and Engineering

Funding Sponsors:

U.S. Department of Energy, Office of Science, Basic Energy Sciences under Award #DE-SC0020384 U.S. Department of Defence through a Science, Mathematics, and Research for Transformation (SMART) scholarship

Owner

  • Name: EAGG - University of Florida
  • Login: EAGG-UF
  • Kind: organization

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
  - family-names: Yan
    given-names: Weishi
  - family-names: Melivlle
    given-names: Joseph 
  - family-names: Krause
    given-names: Amanda
  - family-names: Tonks
    given-names: Michael
  - family-names: Harley
    given-names: Joel
title: "Physics-Regulated Interpretable Machine Learning Microstructure Evolution (PRIMME)"
version: 1.0
date-released: 2022-03-08
url: "https://github.com/EAGG-UF/PRIMME"

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Last Year
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