Skip to main content

Physics-informed neural networks

Project description

PyPI version

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: 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).


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
cd pinn
pip install -e .

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for pml-pinn, version 0.0.2
Filename, size File type Python version Upload date Hashes
Filename, size pml_pinn-0.0.2-py3-none-any.whl (16.0 kB) File type Wheel Python version py3 Upload date Hashes View
Filename, size pml-pinn-0.0.2.tar.gz (9.6 kB) File type Source Python version None Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page