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.


References in zbMATH (referenced in 33 articles )

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  1. Allen, Jeffery M.; Chang, Justin; Usseglio-Viretta, Francois L. E.; Graf, Peter; Smith, Kandler: A segregated approach for modeling the electrochemistry in the 3-D microstructure of li-ion batteries and its acceleration using block preconditioners (2021)
  2. Bastian, Peter; Blatt, Markus; Dedner, Andreas; Dreier, Nils-Arne; Engwer, Christian; Fritze, René; Gräser, Carsten; Grüninger, Christoph; Kempf, Dominic; Klöfkorn, Robert; Ohlberger, Mario; Sander, Oliver: The \textscDuneframework: basic concepts and recent developments (2021)
  3. Kamensky, David: Open-source immersogeometric analysis of fluid-structure interaction using FEniCS and tIGAr (2021)
  4. Zimmerman, Alexander G.; Kowalski, Julia: Mixed finite elements for convection-coupled phase-change in enthalpy form: open software verified and applied to 2D benchmarks (2021)
  5. Chen, Tyler; Carson, Erin: Predict-and-recompute conjugate gradient variants (2020)
  6. Nennig, Benoit; Perrey-Debain, Emmanuel: A high order continuation method to locate exceptional points and to compute Puiseux series with applications to acoustic waveguides (2020)
  7. Cimrman, Robert; Lukeš, Vladimír; Rohan, Eduard: Multiscale finite element calculations in Python using sfepy (2019)
  8. Cotter, Colin; Crisan, Dan; Holm, Darryl D.; Pan, Wei; Shevchenko, Igor: Numerically modeling stochastic Lie transport in fluid dynamics (2019)
  9. Gjerde, Ingeborg G.; Kumar, Kundan; Nordbotten, Jan M.; Wohlmuth, Barbara: Splitting method for elliptic equations with line sources (2019)
  10. Joshaghani, M. S.; Chang, J.; Nakshatrala, K. B.; Knepley, M. G.: Composable block solvers for the four-field double porosity/permeability model (2019)
  11. Kamensky, David; Bazilevs, Yuri: \textsctIGAr: automating isogeometric analysis with \textscFEniCS (2019)
  12. Maddison, James R.; Goldberg, Daniel N.; Goddard, Benjamin D.: Automated calculation of higher order partial differential equation constrained derivative information (2019)
  13. Ruiz-Gironés, Eloi; Roca, Xevi: Imposing boundary conditions to match a CAD virtual geometry for the mesh curving problem (2019)
  14. Budd, Chris J.; McRae, Andrew T. T.; Cotter, Colin J.: The scaling and skewness of optimally transported meshes on the sphere (2018)
  15. 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)
  16. 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)
  17. Garcia, D.; Ghommem, M.; Collier, N.; Varga, B. O. N.; Calo, V. M.: PyFly: a fast, portable aerodynamics simulator (2018)
  18. Kirby, Robert C.: A general approach to transforming finite elements (2018)
  19. Kirby, Robert C.; Mitchell, Lawrence: Solver composition across the PDE/linear algebra barrier (2018)
  20. McRae, Andrew T. T.; Cotter, Colin J.; Budd, Chris J.: Optimal-transport -- based mesh adaptivity on the plane and sphere using finite elements (2018)

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