rssn: A High-Performance Scientific Computing Library for Rust

rssn is an open-source scientific computing library for Rust, combining a high-performance symbolic computation engine with numerical methods and physics simulations.

At its core, rssn utilizes a Directed Acyclic Graph (DAG) to represent mathematical expressions, ensuring that they are always in a canonical form. This allows for highly efficient memory use and computational speed.

A Note from the Author

As the primary author, I extend my deepest gratitude for your interest in this project.

I am a high school student in mainland China with a profound passion for this field (and a deep interest in high-energy physics theory, my ultimate academic goal). Due to my demanding academic commitments, my time is limited, and my responses to issues and pull requests may sometimes be delayed.

I sincerely appreciate your patience and understanding, and I warmly welcome every contribution from the community. I aim to provide regular project updates every Sunday (CST), whenever possible.

-- Pana Yang

✨ Key Features

• Efficient DAG-based Expression Model: Expressions are stored as a canonical DAG, ensuring that identical subexpressions are represented by a single node in memory for maximum efficiency.
• Advanced Symbolic Algebra: A powerful Computer Algebra System (CAS) that goes beyond simple simplification:
  • Polynomial Algebra: Includes Gröbner basis computation for solving polynomial systems.
  • Simplification with Relations: Can simplify expressions with respect to polynomial side-relations (e.g., simplifying x^2 to 1 - y^2 given x^2 + y^2 - 1 = 0).
• Symbolic Calculus: Functions for differentiation, integration, limits, and series expansion.
• Numerical Methods: A rich collection of algorithms for numerical integration, optimization, and solving differential equations.
• Versatile Output: Render expressions as pretty-printed text, LaTeX, or Typst.
• Stable FFI Interface: A robust C-compatible foreign function interface (cdylib) is available for integration with other languages like Python, C++, and Fortran.

🚀 Quick Start

Add rssn to your Rust project:

cargo add rssn

Then, perform a simple symbolic differentiation:

use rssn::symbolic::core::Expr;
use rssn::symbolic::calculus::differentiate;

// Define a variable 'x'
let x = Expr::new_variable("x");

// Define the expression: sin(x)
let expr = Expr::new_sin(x);

// Differentiate with respect to 'x'
let derivative = differentiate(&expr, "x");

// The result will be cos(x)
println!("The derivative of {} is: {}", expr, derivative);

For more advanced examples, such as simplification with relations, please see the API documentation.

🗺️ Roadmap

• v0.1.10 — Finalize core symbolic engine, expand simplification rules.
• v0.2.0 — Stabilization release, expand numerical methods.
• v0.3.0 — Performance improvements & broader algorithm coverage.
• v0.4.0 — Optional FFI for HPC, start development of rsst scripting toolkit.
• v1.0.0 — Full API stabilization.

🤝 Contributing

We welcome contributions of all kinds — bug fixes, performance optimizations, new algorithms, and documentation improvements. See CONTRIBUTING.md for detailed guidelines.

💰 Sponsorship & Donations

Scientific computing requires heavy resources for CI/CD, benchmarking, and cloud testing. You can support development via GitHub Sponsors.

Enterprise sponsors will receive:

• Priority support from the core maintainers
• Ability to request features
• Direct collaboration on integration needs

Excess donations will be redirected to upstream Rust ecosystem projects (e.g., rust foundation) or community initiatives.

Updates: Due to temporary issues, GitHub Sponsors is currently unavailable. If you would like to make a donation, please use PayPal to donate to @panayang338.

👥 Maintainers & Contributors

• Author: Pana Yang (ORCID: 0009-0007-2600-0948, email: Pana.Yang@hotmail.com)
• Consultants:
  • X. Zhang (Algorithm & Informatics, @RheaCherry, 3248998213@qq.com)
  • Z. Wang (Mathematics)
  • Y. Li (Physics) (xian1360685019@qq.com)
• Additional contributors: Owen Yang (yangguangyong@gmail.com)

📜 License

Licensed under the Apache 2.0. See LICENSE for details.