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PhysAI is an open-source AI project that aims to link quantum mechanics and general relativity by generating, testing, and improving physical equations. It leverages machine learning, integrates with existing research, generates LaTeX documents, and encourages collaborative learning. It relies on community-driven contributions to improve accuracy.

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PhysAI

PhysAI is an open-source project aimed at developing an AI system that generates, tests, and validates physical equations, with a particular focus on linking quantum mechanics and general relativity. By leveraging machine learning algorithms and integrating them with existing research, PhysAI aims to create accurate and reliable equations that can explain physical phenomena.

Features

  • Data collection and preprocessing from experiments, observations, and simulations
  • AI-driven generation and improvement of physical equations
  • Equation verification through comparison with experimental results and other data sources
  • Collaborative learning platform that encourages contributions from diverse scientific fields
  • LaTeX integration for generating professional, accessible documents
  • Robust testing and continuous integration

Getting Started

Prerequisites

  • Python 3.7+
  • LaTeX distribution (e.g. TeX Live)

Installation

  1. Clone the repository
  2. Install the required Python packages
pip install -r requirements.txt
  1. Install the required LaTeX packages
tlmgr install $(cat tex/requirements.txt)
  1. Install the PhysAI package
python setup.py install

Usage

You can find the User Guide here.

Contributing

PhysAI is currently in the early stages of development. If you are interested in contributing feel free to clone the repository and submit a pull request. If you have any questions or suggestions, please open an issue.

License

This project is licensed under the MIT License - see the LICENSE file for details.

About

PhysAI is an open-source AI project that aims to link quantum mechanics and general relativity by generating, testing, and improving physical equations. It leverages machine learning, integrates with existing research, generates LaTeX documents, and encourages collaborative learning. It relies on community-driven contributions to improve accuracy.

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