DOLFIN is a C++/Python library that functions as the main user interface of FEniCS. A large part of the functionality of FEniCS is implemented as part of DOLFIN. It provides a problem solving environment for models based on partial differential equations and implements core parts of the functionality of FEniCS, including data structures and algorithms for computational meshes and finite element assembly. To provide a simple and consistent user interface, DOLFIN wraps the functionality of other FEniCS components and external software, and handles the communication between these components.

References in zbMATH (referenced in 101 articles , 1 standard article )

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  1. Fumagalli, Ivan; Parolini, Nicola; Verani, Marco: On a free-surface problem with moving contact line: from variational principles to stable numerical approximations (2018)
  2. Huber, Markus; Rüde, Ulrich; Waluga, Christian; Wohlmuth, Barbara: Surface couplings for subdomain-wise isoviscous gradient based Stokes finite element discretizations (2018)
  3. Oh, Duk-Soon; Widlund, Olof B.; Zampini, Stefano; Dohrmann, Clark R.: BDDC algorithms with deluxe scaling and adaptive selection of primal constraints for Raviart-Thomas vector fields (2018)
  4. Oliver Laslett, Jonathon Waters, Hans Fangohr, Ondrej Hovorka: Magpy: A C++ accelerated Python package for simulating magnetic nanoparticle stochastic dynamics (2018) arXiv
  5. Schmidt, Stephan; Schütte, Maria; Walther, Andrea: Efficient numerical solution of geometric inverse problems involving Maxwell’s equations using shape derivatives and automatic code generation (2018)
  6. Walker, Shawn W.: FELICITY: a Matlab/C++ toolbox for developing finite element methods and simulation modeling (2018)
  7. Bakhos, Tania; Kitanidis, Peter K.; Ladenheim, Scott; Saibaba, Arvind K.; Szyld, Daniel B.: Multipreconditioned gmres for shifted systems (2017)
  8. Březina, Jan; Exner, Pavel: Fast algorithms for intersection of non-matching grids using Plücker coordinates (2017)
  9. Kirby, Robert C.: Fast inversion of the simplicial Bernstein mass matrix (2017)
  10. Kolesov, Alexander E.; Vabishchevich, Petr N.: Splitting schemes with respect to physical processes for double-porosity poroelasticity problems (2017)
  11. Maddison, J.R.; Hiester, H.R.: Optimal constrained interpolation in mesh-adaptive finite element modeling (2017)
  12. Řehoř, Martin; Blechta, Jan; Souček, Ondřej: On some practical issues concerning the implementation of Cahn-Hilliard-Navier-Stokes type models (2017)
  13. Van Bockstal, K.; Slodička, M.; Gistelinck, F.: Identification of a memory kernel in a nonlinear integrodifferential parabolic problem (2017)
  14. Zampini, Stefano: Adaptive BDDC deluxe methods for H(curl) (2017)
  15. Zampini, Stefano; Tu, Xuemin: Multilevel balancing domain decomposition by constraints deluxe algorithms with adaptive coarse spaces for flow in porous media (2017)
  16. Burger, Martin; Pietschmann, Jan-Frederik: Flow characteristics in a crowded transport model (2016)
  17. Davis, Jon H.: Methods of applied mathematics with a software overview (2016)
  18. De La Cruz, Luis M.; Ramos, Eduardo: General template units for the finite volume method in box-shaped domains (2016)
  19. Drawert, Brian; Trogdon, Michael; Toor, Salman; Petzold, Linda; Hellander, Andreas: MOLNs: a cloud platform for interactive, reproducible, and scalable spatial stochastic computational experiments in systems biology using pyurdme (2016) ioport
  20. Hans Fangohr, Maximilian Albert, Matteo Franchin: Nmag micromagnetic simulation tool: software engineering lessons learned (2016)

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