References in zbMATH (referenced in 20 articles )

Showing results 1 to 20 of 20.
Sorted by year (citations)

  1. Ghosh, Supriyo; Newman, Christopher K.; Francois, Marianne M.: \textttTusas: a fully implicit parallel approach for coupled phase-field equations (2022)
  2. Gross, B. J.; Kuberry, P.; Atzberger, P. J.: First-passage time statistics on surfaces of general shape: surface PDE solvers using generalized moving least squares (GMLS) (2022)
  3. Heinlein, Alexander; Perego, Mauro; Rajamanickam, Sivasankaran: FROSch preconditioners for land ice simulations of Greenland and Antarctica (2022)
  4. Nathan Bell, Luke N. Olson, Jacob Schroder: PyAMG: Algebraic Multigrid Solvers in Python (2022) not zbMATH
  5. Thiele, Christopher; Riviere, Beatrice: (p)-multigrid with partial smoothing: an efficient preconditioner for discontinuous Galerkin discretizations with modal bases (2022)
  6. Bettencourt, Matthew T.; Brown, Dominic A. S.; Cartwright, Keith L.; Cyr, Eric C.; Glusa, Christian A.; Lin, Paul T.; Moore, Stan G.; Mcgregor, Duncan A. O.; Pawlowski, Roger P.; Phillips, Edward G.; Roberts, Nathan V.; Wright, Steven A.; Maheswaran, Satheesh; Jones, John P.; Jarvis, Stephen A.: EMPIRE-PIC: a performance portable unstructured particle-in-cell code (2021)
  7. Anselmann, Mathias; Bause, Markus: Numerical study of Galerkin-collocation approximation in time for the wave equation (2020)
  8. D’Elia, M.; Phipps, E.; Rushdi, A.; Ebeida, M. S.: Surrogate-based ensemble grouping strategies for embedded sampling-based uncertainty quantification (2020)
  9. Gross, B. J.; Trask, N.; Kuberry, P.; Atzberger, P. J.: Meshfree methods on manifolds for hydrodynamic flows on curved surfaces: a generalized moving least-squares (GMLS) approach (2020)
  10. Domino, Stefan P.; Sakievich, Philip; Barone, Matthew: An assessment of atypical mesh topologies for low-Mach large-eddy simulation (2019)
  11. Thomas, S. J.; Ananthan, S.; Yellapantula, S.; Hu, J. J.; Lawson, M.; Sprague, M. A.: A comparison of classical and aggregation-based algebraic multigrid preconditioners for high-fidelity simulation of wind turbine incompressible flows (2019)
  12. D’Elia, M.; Edwards, H. C.; Hu, J.; Phipps, E.; Rajamanickam, S.: Ensemble grouping strategies for embedded stochastic collocation methods applied to anisotropic diffusion problems (2018)
  13. Esmaily, M.; Jofre, L.; Mani, A.; Iaccarino, G.: A scalable geometric multigrid solver for nonsymmetric elliptic systems with application to variable-density flows (2018)
  14. Lin, P. T.; Shadid, J. N.; Hu, J. J.; Pawlowski, R. P.; Cyr, E. C.: Performance of fully-coupled algebraic multigrid preconditioners for large-scale VMS resistive MHD (2018)
  15. Phipps, E.; D’Elia, M.; Edwards, H. C.; Hoemmen, M.; Hu, J.; Rajamanickam, S.: Embedded ensemble propagation for improving performance, portability, and scalability of uncertainty quantification on emerging computational architectures (2017)
  16. Prokopenko, Andrey; Tuminaro, Raymond S.: An algebraic multigrid method for (Q_2-Q_1) mixed discretizations of the Navier-Stokes equations. (2017)
  17. Adler, James H.; Benson, Thomas R.; Cyr, Eric C.; MacLachlan, Scott P.; Tuminaro, Raymond S.: Monolithic multigrid methods for two-dimensional resistive magnetohydrodynamics (2016)
  18. Ballard, Grey; Siefert, Christopher; Hu, Jonathan: Reducing communication costs for sparse matrix multiplication within algebraic multigrid (2016)
  19. Tuminaro, R.; Perego, M.; Tezaur, I.; Salinger, A.; Price, S.: A matrix dependent/algebraic multigrid approach for extruded meshes with applications to ice sheet modeling (2016)
  20. Verdugo, Francesc; Wall, Wolfgang A.: Unified computational framework for the efficient solution of (n)-field coupled problems with monolithic schemes (2016)