https://github.com/asem000/accelerated-n-dimensional-ode-solvers

Accelerated N-dimensional ODE solvers
https://github.com/asem000/accelerated-n-dimensional-ode-solvers

Last synced: over 1 year ago
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Accelerated N-dimensional ODE solvers

• Host: GitHub
• URL: https://github.com/asem000/accelerated-n-dimensional-ode-solvers
• Owner: ASEM000
• License: mit
• Created: 2020-09-21T12:31:48.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2020-09-21T12:40:55.000Z (almost 6 years ago)
• Last Synced: 2025-01-29T15:13:18.471Z (over 1 year ago)
• Language: Jupyter Notebook
• Size: 137 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Accelerated N-dimensional Runge-Kutta solvers

Accelerated N dimensional Runge Kutta solvers with implementations in numpy , numba and matlab

#### Implementation of N dimensional Euler,RK2,RK3,RK4 and Dormand–Prince Runge kutta in python+numba

On average , the accelerated implementation is **10x** faster than matlab and **20x~40x** faster than numpy implementation

### Comparison plots

![Image](https://i.imgur.com/tA9QHwN.png)

### How to use ?

1. similar to matlab , you need to create a function that accepts t,y and output y vectors of the same shape as the ode degree

2. check the sample implementation included in the jupyter notebook

### Future work 

Cuda implementation