Summation by parts for finite difference approximations for d/dx
Journal of Computational Physics
On Coordinate Transformations for Summation-by-Parts Operators
Journal of Scientific Computing
Stable and Accurate Artificial Dissipation
Journal of Scientific Computing
Stable and accurate schemes for the compressible Navier-Stokes equations
Journal of Computational Physics
A stable and conservative high order multi-block method for the compressible Navier-Stokes equations
Journal of Computational Physics
General explicit difference formulas for numerical differentiation
Journal of Computational and Applied Mathematics
Superconvergent Functional Estimates from Summation-By-Parts Finite-Difference Discretizations
SIAM Journal on Scientific Computing
Output error estimation for summation-by-parts finite-difference schemes
Journal of Computational Physics
On the impact of boundary conditions on dual consistent finite difference discretizations
Journal of Computational Physics
Dual consistency and functional accuracy: a finite-difference perspective
Journal of Computational Physics
Journal of Computational Physics
PDE-constrained optimization with error estimation and control
Journal of Computational Physics
A generalized framework for nodal first derivative summation-by-parts operators
Journal of Computational Physics
Hi-index | 7.32 |
Summation-by-parts (SBP) operators are finite-difference operators that mimic integration by parts. The SBP operator definition includes a weight matrix that is used formally for discrete integration; however, the accuracy of the weight matrix as a quadrature rule is not explicitly part of the SBP definition. We show that SBP weight matrices are related to trapezoid rules with end corrections whose accuracy matches the corresponding difference operator at internal nodes. For diagonal weight matrices, the accuracy of SBP quadrature extends to curvilinear domains provided the Jacobian is approximated with the same SBP operator used for the quadrature. This quadrature has significant implications for SBP-based discretizations; in particular, the diagonal norm accurately approximates the L^2 norm for functions, and multi-dimensional SBP discretizations accurately approximate the divergence theorem.