ALBERTA is an Adaptive multiLevel finite element toolbox using Bisectioning refinement and Error control by Residual Techniques for scientific Applications. ALBERTA, a sequential adaptive finite-element toolbox, is being used widely in the fields of scientific and engineering computation, especially in the numerical simulation of electromagnetics. But the nature of sequentiality has become the bottle-neck while solving large scale problems. So we develop a parallel adaptive finite-element package based on ALBERTA, using ParMETIS and PETSc. The package is able to deal with any problem that ALBERT solved. Furthermore, it is suitable for distributed memory parallel computers including PC clusters

References in zbMATH (referenced in 170 articles , 1 standard article )

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  1. 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)
  2. Bespalov, Alex; Rocchi, Leonardo; Silvester, David: T-IFISS: a toolbox for adaptive FEM computation (2021)
  3. Barrios, Tomás P.; Cascón, J. Manuel; González, María: On an adaptive stabilized mixed finite element method for the Oseen problem with mixed boundary conditions (2020)
  4. Kahle, Christian; Lam, Kei Fong: Parameter identification via optimal control for a Cahn-Hilliard-chemotaxis system with a variable mobility (2020)
  5. Sander, Oliver: DUNE -- the distributed and unified numerics environment (2020)
  6. Dörfler, Willy; Nürnberg, Robert: Discrete gradient flows for General curvature energies (2019)
  7. Garcke, Harald; Hinze, Michael; Kahle, Christian: Optimal control of time-discrete two-phase flow driven by a diffuse-interface model (2019)
  8. Hutridurga, H.; Venkataraman, C.: Heterogeneity and strong competition in ecology (2019)
  9. Kahle, Christian; Lam, Kei Fong; Latz, Jonas; Ullmann, Elisabeth: Bayesian parameter identification in Cahn-Hilliard models for biological growth (2019)
  10. Kimura, Masato; Notsu, Hirofumi; Tanaka, Yoshimi; Yamamoto, Hiroki: The gradient flow structure of an extended Maxwell viscoelastic model and a structure-preserving finite element scheme (2019)
  11. Sváček, Petr: On implementation aspects of finite element method and its application (2019)
  12. Bänsch, E.; Karakatsani, F.; Makridakis, C. G.: A posteriori error estimates for fully discrete schemes for the time dependent Stokes problem (2018)
  13. Barrett, John W.; Garcke, Harald; Nürnberg, Robert: Gradient flow dynamics of two-phase biomembranes: sharp interface variational formulation and finite element approximation (2018)
  14. Deckelnick, Klaus; Styles, Vanessa: Stability and error analysis for a diffuse interface approach to an advection-diffusion equation on a moving surface (2018)
  15. Garcke, Harald; Lam, Kei Fong; Nürnberg, Robert; Sitka, Emanuel: A multiphase Cahn-Hilliard-Darcy model for tumour growth with necrosis (2018)
  16. Garcke, Harald; Lam, Kei Fong; Styles, Vanessa: Cahn-Hilliard inpainting with the double obstacle potential (2018)
  17. Gräßle, Carmen; Hinze, Michael: POD reduced-order modeling for evolution equations utilizing arbitrary finite element discretizations (2018)
  18. Lorenzi, Tommaso; Venkataraman, Chandrasekhar; Lorz, Alexander; Chaplain, Mark A. J.: The role of spatial variations of abiotic factors in mediating intratumour phenotypic heterogeneity (2018)
  19. Madzvamuse, Anotida; Barreira, Raquel: Domain-growth-induced patterning for reaction-diffusion systems with linear cross-diffusion (2018)
  20. Aland, Sebastian; Hahn, Andreas; Kahle, Christian; Nürnberg, Robert: Comparative simulations of Taylor flow with surfactants based on sharp- and diffuse-interface methods (2017)

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