sapsan 0.2.1

Sapsan project

Sapsan

Sapsan is a pipeline for easy Machine Learning implementation in scientific projects. That being said, its primary goal and featured models are geared towards dynamic MHD turbulence subgrid modeling. Sapsan will soon feature Physics-Informed Machine Learning models in its set of tools to accurately capture the turbulent nature applicable to Core-Collapse Supernovae.

Feel free to check out a website version at sapsan.app. The interface is indentical to the GUI of the local version of Sapsan, except lacking the ability to edit the model code on the fly.

Note: currently Sapsan is in alpha, but we are actively working on it and introduce new feature on a daily basis.

Sapsan's Wiki

Please refer to Sapsan's github wiki to learn more about framework's details and capabilities.

Quick Start

1. Clone from git (recommended)

git clone https://github.com/pikarpov-LANL/Sapsan.git
cd Sapsan/
python setup.py install

For GPU enabled version change the last line to

python setup_gpu.py install

2. Install via pip (cpu-only)

pip install sapsan

Note: make sure you are using the latest release version

Run Examples

To make sure everything is alright and to familiarize yourself with the interface, please run the following CNN example on 3D data:

jupyter notebook sapsan/examples/cnn_example.ipynb

or a KRR example on 2D data:

jupyter notebook sapsan/examples/krr_example.ipynb

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Sapsan has a BSD-style license, as found in the LICENSE file.

© (or copyright) 2019. Triad National Security, LLC. All rights reserved. This program was produced under U.S. Government contract 89233218CNA000001 for Los Alamos National Laboratory (LANL), which is operated by Triad National Security, LLC for the U.S. Department of Energy/National Nuclear Security Administration. All rights in the program are reserved by Triad National Security, LLC, and the U.S. Department of Energy/National Nuclear Security Administration. The Government is granted for itself and others acting on its behalf a nonexclusive, paid-up, irrevocable worldwide license in this material to reproduce, prepare derivative works, distribute copies to the public, perform publicly and display publicly, and to permit others to do so.