xitorch: differentiable scientific computing library

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:

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: Linear algebra and sparse linear algebra module
• optimize: Optimization and root finder module
• integrate: Quadrature and integration module
• interpolate: Interpolation

Requirements

• python >=3.8.1,<3.12
• pytorch 1.13.1 or higher (install here)

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, so you will require pip >= 23.1

Used in

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

Gallery

Neural mirror design (example 01):

Initial velocity optimization in molecular dynamics (example 02):