Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Physical Symbolic Optimization
| Date | Stars |
|---|---|
| 2026-07-24 | 1970 |
| 2026-07-25 | 1970 |
| 2026-07-28 | 1970 |
| 2026-07-30 | 1970 |
| 2026-08-06 | 1970 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# $\Phi$-SO : Physical Symbolic Optimization  Physical symbolic optimization ( $\Phi$-SO ) - A symbolic optimization package built for physics. [](https://github.com/WassimTenachi/PhySO) [](https://physo.readthedocs.io/en/latest/?badge=latest) [](https://coveralls.io/github/WassimTenachi/PhySO?branch=main) [](https://twitter.com/WassimTenachi) [](https://arxiv.org/abs/2303.03192) [](https://arxiv.org/abs/2312.01816) [](https://badge.fury.io/py/physo) [](https://anaconda.org/conda-forge/physo) Source code: [WassimTenachi/PhySO](https://github.com/WassimTenachi/PhySO)\ Documentation: [physo.readthedocs.io](https://physo.readthedocs.io/en/latest/) ## What's New ✨ **2025-08** : 📦 Install via `pip install physo` and `conda` now available! **2025-07** : 🐍 Python 3.12 + latest `NumPy`/`PyTorch`/`SymPy` support. **2024-06** : 📚 Full documentation overhaul. **2024-05** : 🔬 **Class SR**: Multi-dataset symbolic regression. **2024-02** : 🎯 Uncertainty-aware fitting. **2023-08** : ⚡ Dimensional analysis acceleration. **2023-03** : 🌟 **PhySO** initial release (physics-focused SR). ## Highlights $\Phi$-SO's symbolic regression module uses deep reinforcement learning to infer analytical physical laws that fit data points, searching in the space of functional forms. `physo` is able to leverage: * Physical units constraints, reducing the search space with dimensional analysis ([[Tenachi et al 2023]](https://arxiv.org/abs/2303.03192)) * Class constraints, searching for a single analytical functional form that accurately fits multiple datasets - each governed by its own (possibly) unique set of fitting parameters ([[Tenachi et al 2024]](https://arxiv.org/abs/2312.01816)) $\Phi$-SO recovering the equation for a damped harmonic oscillator: https://github.com/WassimTenachi/PhySO/assets/63928316/655b0eea-70ba-4975-8a80-00553a6e2786 Performances on the standard Feynman benchmark from [SRBench](https://github.com/cavalab/srbench/tree/master)) comprising 120 expressions from the Feynman Lectures on Physics against popular SR packages. $\Phi$-SO achieves state-of-the-art performance in the presence of noise (exceeding 0.1%) and shows robust performances even in the presence of substantial (10%) noise:  # Installation The package has been tested on: - Linux - OSX (ARM & Intel) - Windows If you are encountering issues with the installation, [installing from the source](https://physo.readthedocs.io/en/latest/r_installation.html#source-install) should help. If you are still having issues, please refer to the [FAQ](https://physo.readthedocs.io/en/latest/r_faq.html) or raise an issue on the [GitHub repository](https://github.com/WassimTenachi/PhySO/issues). ## Installing with pip Installing `physo` from PyPI : ```bash pip install physo ``` ## Installing with conda Installing `physo` using conda: ```bash conda install -c conda-forge physo ``` ## Getting started (SR) In this tutorial, we show how to use `physo` to perform Symbolic Regression (SR). The reference notebook for this tutorial can be found here: [📙sr_quick_start.ipynb](https://github.com/WassimTenachi/PhySO/blob/main/demos/sr_quick_start.ipynb). ### Setup Importing the nec
Excerpt of 15,975 characters
Read on GitHub
1.1k
7
Felix Yan · Arch Linux @archlinux · China
1
1
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:04aecf9da2ff18cf, topic:reinforcement-learning, readme:reinforcement learning
matched fp:04aecf9da2ff18cf, topic:deep-learning