Metadata-Version: 2.1
Name: pml-pinn
Version: 0.0.1
Summary: Physics-informed neural networks
Home-page: https://github.com/PML-UCF/pinn
Author: Felipe Viana, Renato G. Nascimento
Author-email: viana@ucf.edu, renato.gn@knights.ucf.edu
License: MIT
Description: # Physics-informed neural networks
        Welcome to the PML repository for physics-informed neural networks. We will use this repository to disseminate our research in this exciting topic. Links for some useful publications:
        * [**Fleet Prognosis with Physics-informed Recurrent Neural Networks:**](https://arxiv.org/abs/1901.05512) This paper introduces a novel physics-informed neural network approach to prognosis by extending recurrent neural networks to cumulative damage models. We propose a new recurrent neural network cell designed to merge physics-informed and data-driven layers. With that, engineers and scientists have the chance to use physics-informed layers to model parts that are well understood (e.g., fatigue crack growth) and use data-driven layers to model parts that are poorly characterized (e.g., internal loads).
        
        ## Install
        
        To install the stable version just do:
        ```
        pip install pml-pinn
        ```
        
        ### Develop mode
        
        To install in develop mode, clone this repository and do a pip install:
        ```
        git clone https://github.com/PML-UCF/pinn.git
        cd pinn
        pip install -e .
        ```
Keywords: physics informed,neural networks,machine learning,deep learning,tensorflow,keras,python
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Education
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Description-Content-Type: text/markdown
