Balaje Kalyanaraman
Home Resume Research Blog GSoC WIP


Postdoc at Umeå University, Sweden

2 November 2019

A note on Gradient Recovery for the Virtual Element Method

I recently published a paper on Gradient Recovery technique based on oblique projection for the virtual element method in the ANZIAM journal as a part of CTAC proceedings [1]. The idea behind the gradient recovery technique is to project the virtual element gradient onto the \(L^2\) space using an oblique projection. You can find the finite element equivalent, for example, in this paper [3].

We proposed that the bi-orthogonal basis \(\mu_j\) satisfy the relation

\[\begin{equation} \int_K \Pi^0_K\varphi_i \Pi^0_K \mu_j = c_j\delta_{ij} = A^K. \end{equation}\]

and then the gradient recovery operator as \(\text{Q}_h\left(\frac{\partial u_h}{\partial x_k}\right) \in V_h\) for \(k=1,2\) such that

\[\begin{equation} \sum_{K}\left(\Pi_K^{0}g_h^k, \Pi_K^{0} \mu_j\right)_K = \sum_{K}\left(\frac{\partial (\Pi_K^{0} u_h)}{\partial x_k}, \Pi_K^{0}\mu_j\right)_K. \end{equation}\]

In the paper, we have shown how this projection can be used to compute the right hand side of the above equation explicitly without computing the bi-orthogonal bases. The method worked beautifully yielding a super-convergence rate of \(\approx 1.5\) on quasi-uniform meshes. However, there is a slight trouble in the definition of the bi-orthogonal bases. We see that, using the ansatz

\[\begin{equation} \Pi^0_K \varphi_i = \sum_{\alpha=1}^{n_k} s_i^\alpha m_\alpha, \end{equation}\]

and the fact that \(m_\alpha \in V_h\), we have

\[\begin{eqnarray} \int_K \Pi^0_K\varphi_i \Pi^0_K \mu_j = \sum_{\alpha=1}^{n_k} s_i^\alpha \sum_{r=1}^N \text{dof}_r(m_\alpha) \int_K \Pi^0_K\varphi_i \Pi^0_K \mu_j, \end{eqnarray}\]

which in matrix form yields

\[A^K = M\Pi^0_* \,A^K,\]

where \(M = \text{dof}_r(m_\alpha)\). This does not always hold true! So now we must redefine the bi-orthogonal bases and the projection operator. The redefinition does not yield a huge difference in computation except the formation of a new stability type term which is crucial to ensure good mathematical properties.

Redefinition of the gradient recovery

Let us redefine the bi-orthogonal bases \(\mu_j\) as a set of functions satisfying

\[\int_K \varphi_i \,\mu_j = c_j\delta_{ij} = D^K,\]

and the gradient recovery operator as \(\text{Q}_h\left(\frac{\partial u_h}{\partial x_k}\right) \in V_h\) for \(k=1,2\) such that

\[\begin{equation} \sum_{K}\left(g_h^k, \mu_j\right)_K = \sum_{K}\left(\frac{\partial u_h}{\partial x_k}, \mu_j\right)_K. \end{equation}\]

We note that this definition projects the gradient itself rather than the \(L^2-\) projection and is more rigorous than the definition presented in the paper. Now we describe how to compute this gradient recovery.

The linear system

The left hand side in the gradient recovery equation can be written as

\[\begin{equation} \sum_{K}\left(g_h^k, \mu_j\right)_K = \sum_K \sum_{i=1}^{N} \text{dof}(g_h^k)\, (\varphi_i, \mu_j)_K. \end{equation}\]

The right side can be split into two terms as follows.

\[\begin{eqnarray} \sum_{K}\left(\frac{\partial u_h}{\partial x_k}, \mu_j\right)_K &=& \sum_{K}\sum_{i=1}^N\text{dof}_i(u_h)\left(\frac{\partial \varphi_i}{\partial x_k}, \mu_j\right)_K,\\ &=& \sum_{K}\sum_{i=1}^N\text{dof}_i(u_h)\left(\frac{\partial (\Pi^0_K \varphi_i)}{\partial x_k}, \mu_j\right)_K + \sum_{K}\sum_{i=1}^N\text{dof}_i(u_h)\left(\frac{\partial (\text{I}-\Pi^0_K)\varphi_i}{\partial x_k}, \mu_j\right)_K. \end{eqnarray}\]

which can be written as a matrix system

\[[D]\left\{g_h^k\right\} = [Q_k]\left\{u_h\right\} + [S_k]\left\{u_h\right\},\]

where the vector \(\left\{u_h\right\} = \text{dof}_i(u_h)\) is known.

The first term can be computed exactly using the definition of the bi-orthogonal bases as

\[\begin{eqnarray} \left(\frac{\partial (\Pi^0_K\varphi_i)}{\partial x_k}, \mu_j\right)_K &=& \sum_{\alpha=1}^{n_k} s_i^\alpha \left(\frac{\partial m_\alpha}{\partial x_k},\mu_j\right)_K,\\ &=& \sum_{\alpha=1}^{n_k} s_i^\alpha \sum_{j=1}^N \text{dof}_j\left(\frac{\partial m_\alpha}{\partial x_k}\right) \left(\varphi_i,\mu_j\right)_K.\\ \end{eqnarray}\]

Thus the global matrix can be written as \(Q_k = \sum_K Q_k^K\) with the element level matrix given by

\[Q_k^K = D^K M_k \Pi^0_*,\]

where the entries of \(M_k = \text{dof}\left(\frac{\partial m_\alpha}{\partial x_k}\right)\) and \(\Pi^0_* = s_i^\alpha\).

The second term however, cannot be computed exactly and must be approximated. This acts as a stability term for the gradient recovery operator. We have

\[\left(\frac{\partial (\text{I}-\Pi^0_K)\varphi_i}{\partial x_k}, \mu_j\right)_K \approx \frac{|K|}{h_K} \sum_{k=1}^{N} \text{dof}_k\left\{(\text{I}-\Pi^0_K)\varphi_i\right\} \, \text{dof}_k(\mu_j).\]

We are still not complete. The term \(\text{dof}_k(\mu_j)\) can be evaluated since the function is not known on the element/boundaries. However, we can see that using the definition of the bi-orthogonal bases, we can write

\[\begin{eqnarray} D^K &=& \int_K \left(\Pi^0_K\varphi_i + (\text{I}-\Pi^0_K)\varphi_i\right)\, \mu_j,\\ &=& \int_K \Pi^0_K\varphi_i\,\mu_j + \int_K(\text{I}-\Pi^0_K\varphi_i)\, \mu_j.\\ \end{eqnarray}\]

For the above equation, using similar arguments for the first term, we can construct the matrix equation

\[\begin{eqnarray*} M\Pi^0_*D^K + T_K &=& D^K\quad \text{or}\\ T_K &=& (\text{I}-M\Pi^0_*) D_K. \end{eqnarray*}\]

where \(M = \text{dof}(m_\alpha)\). Thus, we have

\[\begin{eqnarray*} \sum_{k=1}^{N} \text{dof}_k\left\{(\text{I}-\Pi^0_K)\varphi_i\right\} \, \text{dof}_k(\mu_j) \approx \frac{1}{|K|}T_K \end{eqnarray*}\]

and therefore

\[S_k^K = \left(\frac{\partial (\text{I}-\Pi^0_K)\varphi_i}{\partial x_k}, \mu_j\right)_K \approx \frac{1}{h_K}T_K\]

and \(S_k = \sum_K S_k^K\).

Thus the redefined gradient recovery can be performed by solving the linear system

\[\begin{eqnarray} [D]\left\{g_h^k\right\} &=& [QS]\left\{u_h\right\},\\ \left\{g_h^k\right\} &=& [D]^{-1}[QS]\left\{u_h\right\}. \end{eqnarray}\]


\[QS = \sum_K \left(D^K\,M_k\Pi^0_* + \frac{1}{h_K}T_K\right)\]

and the sum denotes a finite element assembly over the elements \(K\). The diagonal matrix \(D\) can be easily inverted thus retaining the computational efficiency.


Now the question is - What is the difference between the redefined method and the earlier method? The answers are:

  1. The redefined method is a natural extension of the finite element gradient recovery using oblique projection.

  2. The redefined method has an extra stability term that is absent in the method proposed in the paper. This is crucial for mathematically good properties to be ensured. The older method can be recovered by simply ignoring the stability term in the redefined method.

The effect of the stability term must be studied in detail and I hope to write a new post on it soon!


[1] B. Kalyanaraman, B. P. Lamichhane, M. Meylan. “A gradient recovery method based on an oblique projection for virtual element method”. In: ANZIAM Journal. 60 (2018), pp. 187-200.

[2] L. Beirão da Veiga, F. Brezzi, L. D. Marini, and A. Russo. “The hitchhiker’s guide to the virtual element method”. In: Math. Mod. Meth. Appl. Sci. 24.08 (2014), pp. 1541–1573.

[3] B. P. Lamichhane. “A gradient recovery operator based on an oblique projection”. In: Electron. Trans. Numer. Anal. 37 (2010), pp. 166–172.

tags: vem - gradient - recovery © 2019- Balaje Kalyanaraman. Hosted on Github pages. Based on the Minimal Theme by orderedlist