When utilizing Galerkin-type solutions for IBVPs, we often have to compute integrals using numerical methods such as Gauss quadrature. In such a solution, we solve for the values of a function at mesh nodes, whereas the integration takes place at the quadrature points. Depending on the case, we may need to compute the values of a function at mesh nodes, given their values at quadrature points, e.g. stress recovery for mechanical problems.

There are many ways of achieving this, such as superconvergent patch recovery. In this post, I wanted to document a widely used solution which is easy to implement, and which is used in research oriented codebases such as FEAP.

L2 Projection

Given a function $u \in L^2(\Omega)$ , its projection into a finite element space $V_h\subset L^2(\Omega)$ is defined through the following optimization problem:

Find $u_h\in V_h$ such that

\begin{equation} \Pi(u_h) := \frac{1}{2}\lVert u_h-u \rVert^2_{L^2(\Omega)} \quad\rightarrow\quad \text{min} \tag{1}\end{equation}

There is a unique solution to the problem since $\Pi(\cdot)$ is convex. Taking its variation, we have $ $\begin{equation} D \Pi(u_h) \cdot v_h = \langle u_h-u, v_h \rangle = 0 \tag{2}\end{equation}$ $

for all $v_h\in V_h$ . Thus we have the following variational formulation

Find $u_h\in V_h$ such that
$\begin{equation} \langle u_h,v_h\rangle = \langle u, v_h\rangle \tag{3}\end{equation}$
for all $v_h\in V_h$ .

Here,

\begin{equation} \begin{alignedat}{3} m(u_h,v_h) &= \langle u_h,v_h\rangle && = \int_\Omega u_hv_h \,dx \quad\text{and} \\ b(v_h) &= \langle u, v_h\rangle && = \int_\Omega u v_h \,dx \end{alignedat} \tag{4}\end{equation}

are our bilinear and linear forms respectively. Substituting FE discretizations $u_h = \sum_{J=1}^{\nnode} u^JN^J$ and $v_h = \sum_{I=1}^{\nnode} v^IN^I$ , we have

\begin{equation} \suml{J=1}{\nnode} M^{I\!J} u^J = b^I \htmlId{eq:projectionsystem1}{} \tag{5}\end{equation}

for $I=1,\dots,\nnode$ , where the FE matrix and vector are defined as

\begin{equation} \begin{alignedat}{3} M^{I\!J} &= m(N^J,N^I) &&= \int_\Omega N^JN^I \,dx \quad\text{and} \\ b^{I} &= b(N^I) &&= \int_\Omega u N^I \,dx \end{alignedat} \tag{6}\end{equation}

Thus L2 projection requires the solution of a linear system

\boldsymbol{M}\boldsymbol{u}=\boldsymbol{b}

which depending on the algorithm used, can have a complexity of at least $O(n^2)$ and at most $O(n^3)$ .

Lumped L2 Projection

The L2 projection requires the solution of a system which can be computationally expensive. It is possible to convert the matrix—called the mass matrix in literature—to a diagonal form through a procedure called lumping.

The operator for row summation is defined as

\begin{equation} \rowsum{(\cdot)}_i := \suml{j=1}{\nnode} (\cdot)_{ij} \tag{7}\end{equation}

For the mass matrix, we have

\begin{equation} \rowsum M^{I} = \suml{J=1}{\nnode} \int_\Omega N^JN^I \,dx = \int_\Omega N^I \,dx =: m^I \tag{8}\end{equation}

since $\sum_{J=1}^{\nnode} N^J = 1$ . Substituting the lumped mass matrix allows us to decouple the linear system of equations in (5) and instead write

\begin{equation} m^I u^I = b^I \tag{9}\end{equation}

for $I=1,\dots,\nnode$ . The lumped L2 projection is then as simple as

\begin{equation} u^I = \frac{b^I}{m^I} = \frac{\displaystyle\int_\Omega u N^I\,dx}{\displaystyle\int_\Omega N^I \,dx} \tag{10}\end{equation}

This results in a very efficient algorithm with $O(n)$ complexity.

Conclusion

Lumped L2 projection is a faster working approximation to L2 projection that is easy to implement for quick results. You can use it when developing a solution for an IBVP, and don’t want to wait too long when debugging, while not forgetting that it introduces some error.