The FEniCS Project is a collaborative project for the development of innovative concepts and tools for automated scientific computing, with a particular focus on automated solution of differential equations by finite element methods. FEniCS has an extensive list of features for automated, efficient solution of differential equations, including automated solution of variational problems, automated error control and adaptivity, a comprehensive library of finite elements, high performance linear algebra and many more.

References in zbMATH (referenced in 118 articles )

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  1. Abali, Bilen Emek: Computational reality. Solving nonlinear and coupled problems in continuum mechanics (2017)
  2. Arnold, Douglas N.; Chen, Hongtao: Finite element exterior calculus for parabolic problems (2017)
  3. Chang, J.; Karra, S.; Nakshatrala, K.B.: Large-scale optimization-based non-negative computational framework for diffusion equations: parallel implementation and performance studies (2017)
  4. Gunzburger, Max; Jiang, Nan; Schneier, Michael: An ensemble-proper orthogonal decomposition method for the nonstationary Navier-Stokes equations (2017)
  5. Hu, Kaibo; Ma, Yicong; Xu, Jinchao: Stable finite element methods preserving $\nabla \cdot \boldsymbolB=0$ exactly for MHD models (2017)
  6. Lee, Jeonghun J.; Mardal, Kent-Andre; Winther, Ragnar: Parameter-robust discretization and preconditioning of Biot’s consolidation model (2017)
  7. Ames, Ellery; Andréasson, Håkan; Logg, Anders: On axisymmetric and stationary solutions of the self-gravitating Vlasov system (2016)
  8. Anzt, Hartwig; Chow, Edmond; Saak, Jens; Dongarra, Jack: Updating incomplete factorization preconditioners for model order reduction (2016)
  9. Bauman, Paul T.; Stogner, Roy H.: GRINS: a multiphysics framework based on the libMesh finite element library (2016) ioport
  10. Bommer, Vera; Yousept, Irwin: Optimal control of the full time-dependent Maxwell equations (2016)
  11. Burger, Martin; Pietschmann, Jan-Frederik: Flow characteristics in a crowded transport model (2016)
  12. Burstedde, Carsten; Holke, Johannes: A tetrahedral space-filling curve for nonconforming adaptive meshes (2016)
  13. Cotter, Colin J.; Kirby, Robert C.: Mixed finite elements for global tide models (2016)
  14. Davis, Jon H.: Methods of applied mathematics with a software overview (2016)
  15. de Hoop, Maarten V.; Kepley, Paul; Oksanen, Lauri: On the construction of virtual interior point source travel time distances from the hyperbolic Neumann-to-Dirichlet map (2016)
  16. de los Reyes, Juan Carlos; Herzog, Roland; Meyer, Christian: Optimal control of static elastoplasticity in primal formulation (2016)
  17. 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
  18. Elfverson, Daniel; Hellman, Fredrik; Målqvist, Axel: A multilevel Monte Carlo method for computing failure probabilities (2016)
  19. Elvetun, Ole Løseth; Nielsen, Bjørn Fredrik: PDE-constrained optimization with local control and boundary observations: robust preconditioners (2016)
  20. Gao, Huadong: Unconditional optimal error estimates of BDF-Galerkin FEMs for nonlinear thermistor equations (2016)

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Further publications can be found at: http://fenicsproject.org/citing/