#differential-equations #derive #condition

macro differential-equations-derive

Derives proc macros for the differential-equations crate

1 unstable release

Uses new Rust 2024

new 0.1.1 Apr 28, 2025
0.1.0 Apr 20, 2025

#7 in #differential-equations

Download history 103/week @ 2025-04-14 44/week @ 2025-04-21

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Used in differential-equations

Apache-2.0

11KB
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Derive macros for differential equations crate

GitHub Documentation

License

Copyright 2025 Ryan D. Gast

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

differential-equations

crates.io docs.rs

Documentation | Examples | GitHub | Docs.rs | Crates.io


A high-performance library for numerically solving differential equations
for the Rust programming language.


A high-performance library for solving differential equations in Rust, including:

  • Ordinary Differential Equations (ODEs) - Fixed-step and adaptive solvers with comprehensive features including event detection, dense output, and customizable and common recipes for solution output.

    • Initial Value Problems (IVPs) - Solve problems with known initial conditions
  • Stochastic Differential Equations (SDEs) - Includes solvers such as Euler-Maruyama, Milstein, and Runge-Kutta methods for stochastic differential equations.

    • Customizable Noise - User implements a wide variety of noise in SDE implementation to allow for example diagonal, correlation, and multiplicative noise.

Contributing

This library is looking for contributions to bring the future of scientific computing to Rust!

Please see CONTRIBUTING.md for more information on how to contribute to this project.

Dependencies

~210–640KB
~15K SLoC