Non-uniform Discontinuous Galerkin Filters via Shift and Scale
For computational scientists using DG methods, this work provides a unified framework for boundary filtering that is more efficient and stable, though it is an incremental improvement over existing one-sided filters.
The paper develops a general class of position-dependent spline filters (PSIAC) for Discontinuous Galerkin methods, enabling efficient and stable convolution at boundaries. The approach reduces convolution to a single dot product, improving stability and efficiency over numerical integration.
Convolving the output of Discontinuous Galerkin (DG) computations with symmetric Smoothness-Increasing Accuracy-Conserving (SIAC) filters can improve both smoothness and accuracy. To extend convolution to the boundaries, several one-sided spline filters have recently been developed. We interpret these filters as instances of a general class of position-dependent spline filters that we abbreviate as PSIAC filters. These filters may have a non-uniform knot sequence and may leave out some B-splines of the sequence. For general position-dependent filters, we prove that rational knot sequences result in rational filter coefficients. We derive symbolic expressions for prototype knot sequences, typically integer sequences that may include repeated entries and corresponding B-splines, some of which may be skipped. Filters for shifted or scaled knot sequences are easily derived from these prototype filters so that a single filter can be re-used in different locations and at different scales. Moreover, the convolution itself reduces to executing a single dot product making it more stable and efficient than the existing approaches based on numerical integration. The construction is demonstrated for several established and one new boundary filter.