Metadata-Version: 2.1
Name: nangs
Version: 0.1.2
Summary: Solving Partial Differential Equations with Neural Networks
Home-page: https://github.com/juansensio/nangs
Author: Juan B. Pedro
Author-email: sensioai@gmail.com
License: Apache Software License 2.0
Description: # Welcome to nangs
        
        > Solving Partial Differential Equations with Neural Networks.
        
        Nangs is a Python library built on top of Pytorch to solve Partial Differential Equations.
        
        Our objective is to develop a new tool for simulating nature, using Neural Networks as solution approximation to Partial Differential Equations, increasing accuracy and optimziation speed while reducing computational cost.
        
        Read our [paper](https://arxiv.org/abs/1912.04737) to know more.
        
        ## Installing
        
        nangs is on PyPI so you can just run:
        
        `pip install nangs`
        
        You will also need to insall [Pytorch](https://pytorch.org/).
        
        ## Getting Started
        
        - Learn how to work with nangs with our [tutorials](https://github.com/juansensio/nangs/tree/master/tutorials).
        - See nangs in actions with our [examples](https://github.com/juansensio/nangs/tree/master/examples).
        
        ## Copyright
        
        Copyright 2020 onwards, SensioAI. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this project's files except in compliance with the License. A copy of the License is provided in the LICENSE file in this repository.
Keywords: Partial Differential Equations Neural Networks
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.6
Description-Content-Type: text/markdown
