https://github.com/xitorch/xitorch

Differentiable scientific computing library
https://github.com/xitorch/xitorch

linear-algebra machine-learning numerical-calculations pytorch scientific-computing

Last synced: 3 months ago
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Differentiable scientific computing library

• Host: GitHub
• URL: https://github.com/xitorch/xitorch
• Owner: xitorch
• License: mit
• Created: 2020-09-25T12:19:45.000Z (over 3 years ago)
• Default Branch: master
• Last Pushed: 2023-02-24T19:27:56.000Z (over 1 year ago)
• Last Synced: 2024-02-27T05:23:47.518Z (4 months ago)
• Topics: linear-algebra, machine-learning, numerical-calculations, pytorch, scientific-computing
• Language: Python
• Homepage: https://xitorch.readthedocs.io/
• Size: 2.22 MB
• Stars: 126
• Watchers: 6
• Forks: 18
• Open Issues: 10
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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• awesome-stars - xitorch/xitorch - Differentiable scientific computing library (Python)
• awesome-stars - xitorch

README

        
# `xitorch`: differentiable scientific computing library

![Build](https://img.shields.io/github/workflow/status/xitorch/xitorch/CI)

[![Docs](https://img.shields.io/readthedocs/xitorch)](https://xitorch.readthedocs.io/)

[![Code coverage](https://img.shields.io/codecov/c/github/xitorch/xitorch)](https://codecov.io/gh/xitorch/xitorch)

`xitorch` is a PyTorch-based library of differentiable functions and functionals that

can be widely used in scientific computing applications as well as deep learning.

The documentation can be found at: https://xitorch.readthedocs.io/

## Example

Finding root of a function:

```python

import torch

from xitorch.optimize import rootfinder

def func1(y, A):  # example function

    return torch.tanh(A @ y + 0.1) + y / 2.0

# set up the parameters and the initial guess

A = torch.tensor([[1.1, 0.4], [0.3, 0.8]]).requires_grad_()

y0 = torch.zeros((2, 1))  # zeros as the initial guess

# finding a root

yroot = rootfinder(func1, y0, params=(A,))

# calculate the derivatives

dydA, = torch.autograd.grad(yroot.sum(), (A,), create_graph=True)

grad2A, = torch.autograd.grad(dydA.sum(), (A,), create_graph=True)

```

## Modules

* [`linalg`](xitorch/linalg/): Linear algebra and sparse linear algebra module

* [`optimize`](xitorch/optimize/): Optimization and root finder module

* [`integrate`](xitorch/integrate/): Quadrature and integration module

* [`interpolate`](xitorch/interpolate/): Interpolation

## Requirements

* python >=3.8.1,<3.12

* pytorch 1.13.1 or higher (install [here](https://pytorch.org/))

## Getting started

After fulfilling all the requirements, type the commands below to install `xitorch`

    python -m pip install xitorch

Or to install from GitHub:

    python -m pip install git+https://github.com/xitorch/xitorch.git

Finally, if you want to make an editable install from source:

    git clone https://github.com/xitorch/xitorch.git

    cd xitorch

    python -m pip install -e .

Note that the last option is only available per [PEP 660](https://peps.python.org/pep-0660/), so you will require [pip >= 23.1](https://pip.pypa.io/en/stable/news/#v21-3)

    

## Used in

* Differentiable Quantum Chemistry (DQC): https://dqc.readthedocs.io/

## Gallery

Neural mirror design ([example 01](examples/01-mirror-design/)):

![neural mirror design](examples/01-mirror-design/images/mirror.gif)

Initial velocity optimization in molecular dynamics ([example 02](examples/02-molecular-dynamics/)):

![molecular dynamics](examples/02-molecular-dynamics/images/md.gif)