This software framework implements a NURBS-based Galerkin finite element method (FEM), popularly known as isogeometric analysis (IGA). It is heavily based on PETSc, the Portable, Extensible Toolkit for Scientific Computation. PETSc is a collection of algorithms and data structures for the solution of scientific problems, particularly those modeled by partial differential equations (PDEs). PETSc is written to be applicable to a range of problem sizes, including large-scale simulations where high performance parallel is a must. PetIGA can be thought of as an extension of PETSc, which adds the NURBS discretization capability and the integration of forms. The PetIGA framework is intended for researchers in the numeric solution of PDEs who have applications which require extensive computational resources.
After installing PETSc,
set appropriate values for PETSC_DIR and PETSC_ARCH in your
environment:
$ export PETSC_DIR=/home/user/petsc $ export PETSC_ARCH=arch-linux2-c-debug
Clone the Git repository hosted at GitHub
$ git clone https://github.com/dalcinl/PetIGA.git
Finally, enter PetIGA top level directory and use make to compile
the code and build the PetIGA library:
$ cd PetIGA $ make all $ make test
PetIGA is designed to be efficient and as such, we do not directly do things like output VTK files suitable for viewing the solution. We do have routines which output the discretization information and solution vectors, but these are in a binary format to minimize I/O time. We have written a python package, igakit which handles post-processing for visualization as well as geometry generation. A series of tutorials in how to use PetIGA and igakit are underway and can be found here.
If you find PetIGA helpful in conducting research projects, we would appreciate a citation to the following article:
@article{PetIGA,
author = "L. Dalcin and N. Collier and P. Vignal and A.M.A. Côrtes and V.M. Calo",
title = "PetIGA: A framework for high-performance isogeometric analysis",
journal = "Computer Methods in Applied Mechanics and Engineering",
volume = "308",
pages = "151--181",
year = "2016",
issn = "0045-7825",
doi = "https://doi.org/10.1016/j.cma.2016.05.011",
}
This project was partially supported by the Extreme Computing Research Center (ECRC), Division of Computer, Electrical, and Mathematical Sciences & Engineering (CEMSE), King Abdullah University of Science and Technology (KAUST).