petsc4py

PETSc for Python: This document describes petsc4py, a Python port to the PETSc libraries. PETSc (the Portable, Extensible Toolkit for Scientific Computation) is a suite of data structures and routines for the scalable (parallel) solution of scientific applications modeled by partial differential equations. It employs the MPI standard for all message-passing communication. This package provides an important subset of PETSc functionalities and uses NumPy to efficiently manage input and output of array data.

Keywords for this software

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References in zbMATH (referenced in 43 articles )

Showing results 21 to 40 of 43.
Sorted by year (citations)
  1. Kamensky, David; Bazilevs, Yuri: \textsctIGAr: automating isogeometric analysis with \textscFEniCS (2019)
  2. Maddison, James R.; Goldberg, Daniel N.; Goddard, Benjamin D.: Automated calculation of higher order partial differential equation constrained derivative information (2019)
  3. Ruiz-Gironés, Eloi; Roca, Xevi: Imposing boundary conditions to match a CAD virtual geometry for the mesh curving problem (2019)
  4. Budd, Chris J.; McRae, Andrew T. T.; Cotter, Colin J.: The scaling and skewness of optimally transported meshes on the sphere (2018)
  5. Chang, Justin; Fabien, Maurice S.; Knepley, Matthew G.; Mills, Richard T.: Comparative study of finite element methods using the time-accuracy-size (TAS) spectrum analysis (2018)
  6. Cimrman, Robert; Novák, Matyáš; Kolman, Radek; Tuma, Miroslav; Plešek, Jiří; Vackář, Jiří: Convergence study of isogeometric analysis based on Bézier extraction in electronic structure calculations (2018)
  7. Garcia, D.; Ghommem, M.; Collier, N.; Varga, B. O. N.; Calo, V. M.: PyFly: a fast, portable aerodynamics simulator (2018)
  8. Kirby, Robert C.: A general approach to transforming finite elements (2018)
  9. Kirby, Robert C.; Mitchell, Lawrence: Solver composition across the PDE/linear algebra barrier (2018)
  10. McRae, Andrew T. T.; Cotter, Colin J.; Budd, Chris J.: Optimal-transport -- based mesh adaptivity on the plane and sphere using finite elements (2018)
  11. Paganini, Alberto; Wechsung, Florian; Farrell, Patrick E.: Higher-order moving mesh methods for PDE-constrained shape optimization (2018)
  12. Shipton, J.; Gibson, T. H.; Cotter, C. J.: Higher-order compatible finite element schemes for the nonlinear rotating shallow water equations on the sphere (2018)
  13. Thomas H. Gibson, Lawrence Mitchell, David A. Ham, Colin J. Cotter: Slate: extending Firedrake’s domain-specific abstraction to hybridized solvers for geoscience and beyond (2018) arXiv
  14. Chang, J.; Nakshatrala, K. B.: Variational inequality approach to enforcing the non-negative constraint for advection-diffusion equations (2017)
  15. Luporini, Fabio; Ham, David A.; Kelly, Paul H. J.: An algorithm for the optimization of finite element integration loops (2017)
  16. Rathgeber, Florian; Ham, David A.; Mitchell, Lawrence; Lange, Michael; Luporini, Fabio; Mcrae, Andrew T. T.; Bercea, Gheorghe-Teodor; Markall, Graham R.; Kelly, Paul H. J.: Firedrake, automating the finite element method by composing abstractions (2017)
  17. Robert C. Kirby, Lawrence Mitchell: Solver composition across the PDE/linear algebra barrier (2017) arXiv
  18. Yamazaki, Hiroe; Shipton, Jemma; Cullen, Michael J. P.; Mitchell, Lawrence; Cotter, Colin J.: Vertical slice modelling of nonlinear Eady waves using a compatible finite element method (2017)
  19. Kraus, Michael; Tassi, Emanuele; Grasso, Daniela: Variational integrators for reduced magnetohydrodynamics (2016)
  20. McRae, A. T. T.; Bercea, G.-T.; Mitchell, L.; Ham, D. A.; Cotter, C. J.: Automated generation and symbolic manipulation of tensor product finite elements (2016)