Efficient mass and stiffness matrix assembly via weighted Gaussian quadrature rules for B-splines

TL;DR

This paper introduces weighted Gaussian quadrature rules for B-spline basis functions that significantly reduce the number of quadrature points needed for mass and stiffness matrix assembly, leading to faster computations.

Contribution

It proposes novel weighted Gaussian quadrature rules that minimize quadrature points while maintaining exactness, improving efficiency over previous methods.

Findings

Reduces quadrature points by a factor of rac{p+1}{2p+1}rom previous methods.

Achieves exact integration for uniform C^1 quadratic and C^2 cubic isogeometric discretizations.

Significantly decreases computational cost of matrix assembly.

Abstract

Calabro et al. (2017) changed the paradigm of the mass and stiffness computation from the traditional element-wise assembly to a row-wise concept, showing that the latter one offers integration that may be orders of magnitude faster. Considering a B-spline basis function as a non-negative measure, each mass matrix row is integrated by its own quadrature rule with respect to that measure. Each rule is easy to compute as it leads to a linear system of equations, however, the quadrature rules are of the Newton-Cotes type, that is, they require a number of quadrature points that is equal to the dimension of the spline space. In this work, we propose weighted quadrature rules of Gaussian type which require the minimum number of quadrature points while guaranteeing exactness of integration with respect to the weight function. The weighted Gaussian rules arise as solutions of non-linear systems of equations. We derive rules for the mass and stiffness matrices for uniform $C^1$ quadratic and $C^2$ cubic isogeometric discretizations. Our rules further reduce the number of quadrature points by a factor of $(\frac{p+1}{2p+1})^d$ when compared to Calabro et al. (2017), $p$ being the polynomial degree and $d$ the dimension of the problem, and consequently reduce the computational cost of the mass and stiffness matrix assembly by a similar factor.