THB-DIFF: a GPU-accelerated differentiable programming framework for THB-spline.

Finished the important parts but lacks comments and documentation. This repository is still a work in progress.

The paper can be accessed here.

Installation

• Install conda (Follow the instructions on this webpage). Although, for MacOS it would be easier to install conda using Homebrew.

• Create a conda environment

  conda create -n THB python=3.10
  

• Activate the conda environment.

  conda activate THB
  

• Install the following dependencies using either pip or conda

  • numpy
  • matplotlib
  • jax
  • jaxlib
  • tqdm
  • numba
  • pyvista

  pip install jax jaxlib numba matplotlib tqdm numba pyvista
  

  Replace jax and jaxlib with pytorch if a pytorch version is preferred.

• To access CUDA-accelerated kernels or c++ functions for THB-spline evaluation using PyTorch, run the following command from the THB_extensions directory.

  python setup.py build
  python setup.py install
  

• Install source code THB-Diff

  pip install .
  

  for editable installtion

  pip install -e .
  

Example Usage

Create B-spline objects which constitute the initial tensor-product

For 2D THB-splines

from THB.datastructures import BSpline, TensorProduct

bs1 = BSpline(knotvector=np.array([0, 0, 0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 1, 1]), degree=2)
bs2 = BSpline(knotvector=np.array([0, 0, 0, 0, 0.2, 0.4, 0.5, 0.6, 0.8, 0.9, 1, 1, 1, 1], degree=3)

tp_2D = TensorProduct([bs1, bs2])

For 3D THB-splines

from THB.datastructures import BSpline, TensorProduct

bs1 = BSpline(knotvector=np.array([0, 0, 0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 1, 1]), degree=2)
bs2 = BSpline(knotvector=np.array([0, 0, 0, 0, 0.2, 0.4, 0.5, 0.6, 0.8, 0.9, 1, 1, 1, 1], degree=3)
bs3 = BSpline(knotvector=np.array([0, 0, 0, 0.2, 0.4, 0.6, 0.8, 1, 1, 1]), degree=2)

tp_3D = TensorProduct([bs1, bs2, bs3])

THB-Diff supports non-uniformly spaced knots as input knotvectors for constituent BSplines. The TensorProduct can have BSplines objects of varied degrees and knotvectors as input for tensor-product construction.

Create a Space object which handles the low-level queries and operations on THB-spline domain and function space, and a THB object to handle high-level operations, GPU-accelerated THB-spline evaluation methods, loss functions, gradient computation and other utilites.

h_space = Space(tensor_product=tp, num_levels=3)
THB = THB_layer(h_space)

documentation work in progress...

Citation

@Article{thbdiff,
author={Moola, Ajith and Balu, Aditya and Krishnamurthy, Adarsh and Pawar, Aishwarya},
title={THB-Diff: a GPU-accelerated differentiable programming framework for THB-splines},
journal={Engineering with Computers},
year={2023},}