https://github.com/askabalan/diffrax

Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/

https://github.com/askabalan/diffrax

Repository

Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/

https://github.com/ASKabalan/diffrax/blob/main/

Diffrax
Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable.

Diffrax is a [JAX](https://github.com/google/jax)-based library providing numerical differential equation solvers.

Features include:

- ODE/SDE/CDE (ordinary/stochastic/controlled) solvers;
- lots of different solvers (including `Tsit5`, `Dopri8`, symplectic solvers, implicit solvers);
- vmappable _everything_ (including the region of integration);
- using a PyTree as the state;
- dense solutions;
- multiple adjoint methods for backpropagation;
- support for neural differential equations.

_From a technical point of view, the internal structure of the library is pretty cool -- all kinds of equations (ODEs, SDEs, CDEs) are solved in a unified way (rather than being treated separately), producing a small tightly-written library._

## Installation

```
pip install diffrax
```

Requires Python 3.9+, JAX 0.4.13+, and [Equinox](https://github.com/patrick-kidger/equinox) 0.10.11+.

## Documentation

Available at [https://docs.kidger.site/diffrax](https://docs.kidger.site/diffrax).

## Quick example

```python
from diffrax import diffeqsolve, ODETerm, Dopri5
import jax.numpy as jnp

def f(t, y, args):
    return -y

term = ODETerm(f)
solver = Dopri5()
y0 = jnp.array([2., 3.])
solution = diffeqsolve(term, solver, t0=0, t1=1, dt0=0.1, y0=y0)
```

Here, `Dopri5` refers to the Dormand--Prince 5(4) numerical differential equation solver, which is a standard choice for many problems.

## Citation

If you found this library useful in academic research, please cite: [(arXiv link)](https://arxiv.org/abs/2202.02435)

```bibtex
@phdthesis{kidger2021on,
    title={{O}n {N}eural {D}ifferential {E}quations},
    author={Patrick Kidger},
    year={2021},
    school={University of Oxford},
}
```

(Also consider starring the project on GitHub.)

## See also: other libraries in the JAX ecosystem

**Always useful**  
[Equinox](https://github.com/patrick-kidger/equinox): neural networks and everything not already in core JAX!  
[jaxtyping](https://github.com/patrick-kidger/jaxtyping): type annotations for shape/dtype of arrays.  

**Deep learning**  
[Optax](https://github.com/deepmind/optax): first-order gradient (SGD, Adam, ...) optimisers.  
[Orbax](https://github.com/google/orbax): checkpointing (async/multi-host/multi-device).  
[Levanter](https://github.com/stanford-crfm/levanter): scalable+reliable training of foundation models (e.g. LLMs).  

**Scientific computing**  
[Optimistix](https://github.com/patrick-kidger/optimistix): root finding, minimisation, fixed points, and least squares.  
[Lineax](https://github.com/patrick-kidger/lineax): linear solvers.  
[BlackJAX](https://github.com/blackjax-devs/blackjax): probabilistic+Bayesian sampling.  
[sympy2jax](https://github.com/patrick-kidger/sympy2jax): SymPy<->JAX conversion; train symbolic expressions via gradient descent.  
[PySR](https://github.com/milesCranmer/PySR): symbolic regression. (Non-JAX honourable mention!)  

**Awesome JAX**  
[Awesome JAX](https://github.com/n2cholas/awesome-jax): a longer list of other JAX projects.  

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