arXiv subjects

Jörg Stiller

Publications and source records attributed to Jörg Stiller.

9 recordsLinked to original sources

arXiv2026

Adaptive multigrid for high-order discontinuous Galerkin methods based on the full approximation scheme

We propose an adaptive multigrid (MG) method for discontinuous Galerkin formulations of elliptic problems using Brandt's full approximation scheme (FAS). Unlike common approaches, this method achieves local $hp$-refinement of hexahedral meshes without the need for hanging nodes. The core component of the FAS-MG method is an overlapping Schwarz smoother, which is optionally accelerated by a Krylov method. This smoother is designed for unstructured curvilinear meshes but maintains a tensor-product structure for fast diagonalization. Numerical experiments demonstrate the exceptional efficiency of the FAS-MG method. Dedicated studies confirm its robustness against high aspect ratios, element deformation, and irregular mesh topology. We also verify its capability for dynamic parallel mesh adaptation using the wave-front benchmark of Červený, Dobrev, and Kolev (SIAM J. Sci. Comp. 41, 2019). Finally, we present preliminary results of extending the method to incompressible Navier-Stokes problems.

math.NA↗

arXiv2026

Local high order space-time adaptive MLSDC

Building upon the semi-implicit multilevel spectral deferred correction (SI-MLSDC) method introduced by Pfister and Stiller [38], this work presents a 1D space-time adaptive high-order method combining discontinuous Galerkin spectral element discretizations with multilevel spectral deferred corrections. The proposed approach enables genuine arbitrary-order accuracy in space and time while dynamically balancing spatial and temporal discretization errors to reduce computational cost. A key contribution is the development of a novel temporal error estimator that in combination with a spectral error estimator in space provides a reliable basis for adaptive refinement decisions. The error estimator is compared with two alternative refinement criteria to assess their impact on accuracy, computational efficiency and their suitability for complex problems. The performance of the adaptive method is demonstrated for nonlinear conservation laws ranging from Burgers' equation to the Euler equations. Numerical results show substantial runtime reductions while maintaining the desired accuracy. In particular, significant computational savings are achieved for Burgers' equation, and challenging benchmark problems such as the Shu-Osher shock-fluctuation benchmark. These results demonstrate the potential of adaptive SI-MLSDC methods for efficient high-order space-time adaptive simulations of complex flow problems.

math.NA↗

arXiv2025

Robust semi-implicit multilevel SDC methods for conservation laws

Semi-implicit multilevel spectral deferred correction (SI-MLSDC) methods provide a promising approach for high-order time integration for nonlinear evolution equations including conservation laws. However, existing methods lack robustness and often do not achieve the expected advantage over single-level SDC. This work adopts the novel SI time integrators from [48] for enhanced stability and extends the single-level SI-SDC method with a multilevel approach to increase computational efficiency. The favourable properties of the resulting SI-MLSDC method are shown by linear temporal stability analysis for a convection-diffusion problem. The robustness and efficiency of the fully discrete method involving a high-order discontinuous Galerkin SEM discretization are demonstrated through numerical experiments for the convection-diffusion, Burgers, Euler and Navier-Stokes equations. The method is shown to yield substantial reductions in fine-grid iterations compared to single-level SI-SDC across a broad range of test cases. Finally, current limitations of the SI-MLSDC framework are identified and discussed, providing guidance for future improvements.

math.NA↗

arXiv2022

Assessment of high-order IMEX methods for incompressible flow

This paper investigates the competitiveness of semi-implicit Runge-Kutta (RK) and spectral deferred correction (SDC) time-integration methods up to order six for incompressible Navier-Stokes problems in conjunction with a high-order discontinuous Galerkin method for space discretization. It is proposed to harness the implicit and explicit RK parts as a partitioned scheme, which provides a natural basis for the underlying projection scheme and yields a straight-forward approach for accommodating nonlinear viscosity. Numerical experiments on laminar flow, variable viscosity and transition to turbulence are carried out to assess accuracy, convergence and computational efficiency. Although the methods of order 3 or higher are susceptible to order reduction due to time-dependent boundary conditions, two third-order RK methods are identified that perform well in all test cases and clearly surpass all second-order schemes including the popular extrapolated backward difference method. The considered SDC methods are more accurate than the RK methods, but become competitive only for relative errors smaller than ca $10^{-5}$.

math.NA↗

arXiv2020

A spectral deferred correction method for incompressible flow with variable viscosity

This paper presents a semi-implicit spectral deferred correction (SDC) method for incompressible Navier-Stokes problems with variable viscosity and time-dependent boundary conditions. The proposed method integrates elements of velocity- and pressure-correction schemes, which yields a simpler pressure handling and a smaller splitting error than the SDPC method of Minion & Saye (J. Comput. Phys. 375: 797-822, 2018). Combined with the discontinuous Galerkin spectral-element method for spatial discretization it can in theory reach arbitrary order of accuracy in time and space. Numerical experiments in three space dimensions demonstrate up to order 12 in time and 17 in space for constant as well as varying, solution-dependent viscosity. Compared to SDPC the present method yields a substantial improvement of accuracy and robustness against order reduction caused by time-dependent boundary conditions.

math.NA↗

arXiv2020

Linearizing the hybridizable discontinuous Galerkin method: A linearly scaling operator

This paper proposes a matrix-free residual evaluation technique for the hybridizable discontinuous Galerkin method requiring a number of operations scaling only linearly with the number of degrees of freedom. The method results from application of tensor-product bases on cuboidal Cartesian elements, a specific choice for the penalty parameter, and the fast diagonalization technique. In combination with a linearly scaling, face-wise preconditioner, a linearly scaling iteration time for a conjugate gradient method is attained. This allows for solutions in 1 $μs$ per unknown on one CPU core - a number typically associated with low-order methods.

math.NA↗

arXiv2018

Scaling to the stars -- a linearly scaling elliptic solver for $p$-multigrid

High-order methods gain increased attention in computational fluid dynamics. However, due to the time step restrictions arising from the semi-implicit time stepping for the incompressible case, the potential advantage of these methods depends critically on efficient elliptic solvers. Due to the operation counts of operators scaling with with the polynomial degree $p$ times the number of degrees of freedom $n_{\mathrm{DOF}}$, the runtime of the best available multigrid solvers scales with $\mathcal{O}( p \cdot n_{\mathrm{DOF}})$. This scaling with $p$ significantly lowers the applicability of high-order methods to high orders. While the operators for residual evaluation can be linearized when using static condensation, Schwarz-type smoothers require their inverses on fixed subdomains. No explicit inverse is known in the condensed case and matrix-matrix multiplications scale with ${p \cdot n_{\mathrm{DOF}}}$. This paper derives a matrix-free explicit inverse for the static condensed operator in a cuboidal subdomain. It scales with $p^3$ per element, i.e. ${n_{\mathrm{DOF}}}$ globally, and allows for a linearly scaling additive Schwarz smoother, yielding a $p$-multigrid cycle with an operation count of $\mathcal{O}(n_{\mathrm{DOF}})$. The resulting solver uses fewer than four iterations for all polynomial degrees to reduce the residual by ten orders and has a runtime scaling linearly with ${n_{\mathrm{DOF}}}$ for polynomial degrees at least up to $48$. Furthermore the runtime is less than one microsecond per unknown over wide parameter ranges when using one core of a CPU, leading to time-stepping for the incompressible Navier-Stokes equations using as much time for explicitly treated convection terms as for the elliptic solvers.

math.NA↗

arXiv2016

Robust multigrid for high-order discontinuous Galerkin methods: A fast Poisson solver suitable for high-aspect ratio Cartesian grids

We present a polynomial multigrid method for nodal interior penalty and local discontinuous Galerkin formulations of the Poisson equation on Cartesian grids. For smoothing we propose two classes of overlapping Schwarz methods. The first class comprises element-centered and the second face-centered methods. Within both classes we identify methods that achieve superior convergence rates, prove robust with respect to the mesh spacing and the polynomial order, at least up to ${P=32}$. Consequent structure exploitation yields a computational complexity of $O(PN)$, where $N$ is the number of unknowns. Further we demonstrate the suitability of the face-centered method for element aspect ratios up to 32.

cs.CE↗

arXiv2016

Factorizing the factorization - a spectral-element solver for elliptic equations with linear operation count

High-order methods gain more and more attention in computational fluid dynamics. However, the potential advantage of these methods depends critically on the availability of efficient elliptic solvers. With spectral-element methods, static condensation is a common approach to reduce the number of degree of freedoms and to improve the condition of the algebraic equations. The resulting system is block-structured and the face-based operator well suited for matrix-matrix multiplications. However, a straight-forward implementation scales super-linearly with the number of unknowns and, therefore, prohibits the application to high polynomial degrees. This paper proposes a novel factorization technique, which yields a linear operation count of just 13N multiplications, where N is the total number of unknowns. In comparison to previous work it saves a factor larger than 3 and clearly outpaces unfactored variants for all polynomial degrees. Using the new technique as a building block for a preconditioned conjugate gradient method resulted in a runtime scaling linearly with N for polynomial degrees $2 \leq p \leq 32$ . Moreover the solver proved remarkably robust for aspect ratios up to 128.

math.NA↗