# intell-sci-comput/PSE

<https://repo.masters-masters.com/en/repos/intell-sci-comput/PSE>

[Home](https://repo.masters-masters.com/en) › [intell-sci-comput](https://repo.masters-masters.com/en/owners/intell-sci-comput) › [PSE](https://repo.masters-masters.com/en/repos/intell-sci-comput/PSE)

intell-sci-comput/PSE has 121 stars on GitHub. Its RepoMasters card: Prototype tier. Official PyTorch implementation of PSE/PSRN: Fast and efficient symbolic…

| Field | Value |
| --- | --- |
| Owner | [intell-sci-comput](https://repo.masters-masters.com/en/owners/intell-sci-comput) |
| Stars | 121 |
| Forks | 17 |
| Language | [Python](https://repo.masters-masters.com/en/languages/python) |
| License | MIT |
| Created | 2024 |
| Topics | ai4science, symbolic-regression, ai-for-science, chaotic-dynamical-systems, explainable-ai, interpretable-machine-learning, mahine-learning, sciml, symbolic, symbolic-computation |
| Tier | [Prototype (100 to 249 ★)](https://repo.masters-masters.com/en/ranks/rush) |
| View on GitHub | [intell-sci-comput/PSE](https://github.com/intell-sci-comput/PSE) |

## Repository

Official PyTorch implementation of PSE/PSRN: Fast and efficient symbolic expression discovery through parallelized symbolic enumeration. Evaluates millions of expressions simultaneously on GPU with automated subtree reuse.

The tier for repos with 100 to 249 ★. Published odds per card pulled: 70%. Rarity: Common.

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Data: GitHub · Illustration drawn by RepoMasters from the repo’s ID and its language color.