https://github.com/argmaster/nneve

Collection of physics related neural networks for solving differential equations.
https://github.com/argmaster/nneve

eigenvalue eigenvalueproblems neural-network physical-computing physics quantum-oscillator schrodinger-equation tensorflow

Last synced: 26 days ago
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Collection of physics related neural networks for solving differential equations.

• Host: GitHub
• URL: https://github.com/argmaster/nneve
• Owner: Argmaster
• License: other
• Created: 2022-05-09T13:08:59.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2022-09-11T08:59:39.000Z (about 2 years ago)
• Last Synced: 2024-09-16T13:41:38.754Z (about 2 months ago)
• Topics: eigenvalue, eigenvalueproblems, neural-network, physical-computing, physics, quantum-oscillator, schrodinger-equation, tensorflow
• Language: Python
• Homepage: https://argmaster.github.io/NNEVE/
• Size: 27.8 MB
• Stars: 0
• Watchers: 2
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
![NNEVE](https://raw.githubusercontent.com/Argmaster/NNEVE/main/docs/img/nneve.jpg)

NNEVE is a collection of neural network based solutions to physics based

problems. As for now only network for quantum oscillator approximation is fully

implemented. Hopefully soon will arrive neural network for solving

Navier-Stokes equation based on limited number of measurement points..

# Installation

This project is uploaded to PyPI as `nneve`, therefore can be installed with

following command

```bash

pip install nneve

```

At least Python 3.7 is required.

# Quick example

To view quantum oscillator approximation for states 1 to 7 you can load

precalculated weights and acquire model object with following snippet:

```python

from matplotlib import pyplot as plt

from nneve.quantum_oscillator.examples import default_qo_network

# acquire network object with precalculated weights

# for quantum oscillator state 1 (base)

network = default_qo_network(state=1)

network.plot_solution()

plt.plot()

```

To manually run learning cycle check out

["How to run QONetwork learning cycle"](https://argmaster.github.io/NNEVE//quantum_oscillator/learning_cycle/)

in Quantum Oscillator section of docs.

# Documentation

Online documentation is available at

[argmaster.github.io/NNEVE/](https://argmaster.github.io/NNEVE/)

To build docs locally run

```

tox -e docs

```