1 unstable release
0.0.1 | Nov 30, 2021 |
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#27 in #calculus
20KB
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Numerical Integration
Algorithms and traits for numerical approximation in the Rust language
The purpose of this crate is to provide a selection of common algorithms for approximating differential equations and to unify them under a common API in order to make the process of using and testing different integration schemes as easy as possible.
How to use
The primary entry point into the API is through the Integrator
,
VelIntegrator
, and AdaptiveIntegrator
traits. Each one has an init()
method and step()
. They are all designed in such a way so that all values
and state of the integrator are stored externally and passed in through method
arguments exclusively.
Each trait represents a different class of algorithms requiring different
kinds of data. The Integrator
trait takes in the current state and time,
the timestep, and a closure/function that can compute the derivative of the
state vector. VelIntegrator
does the same, but also requires a function
that computes the velocity in an admittedly convoluted way. And
AdaptiveIntegrator
takes in a minimum error value instead of a time-step.
In addition to these traits are traits that are like the above but adapted to
not include generics in the function signature so that it can be used as in
dyn
types.
To use, you can either work with the traits generally and pass in a particular implementor, or you can just use the various algorithms directly.
At the moment, this crate includes Velocity Verlet and methods in the Runge-Kutta family (including Euler and RK4), but others (such as the linear multistep methods) could be added if enough people want them.
Current state of the project
This project is currently in hiatus for now, and it will probably remain as such unless enough people express interest in it to be continued. If that does happen though, expect the API to have breaking changes, as I am not quite satisfied with the current design and certain features I wish to add require a slight redesign.
Dependencies
~315KB