Differentiable scientific computing library
Project description
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 moduleoptimize
: Optimization and root finder moduleintegrate
: Quadrature and integration moduleinterpolate
: 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):
Project details
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