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 145 articles , 2 standard articles )

Showing results 1 to 20 of 145.
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  1. Abali, Bilen Emek: Computational reality. Solving nonlinear and coupled problems in continuum mechanics (2017)
  2. Allen, Jeffery; Leibs, Chris; Manteuffel, Tom; Rajaram, Harihar: A fluidity-based first-order system least-squares method for ice sheets (2017)
  3. Anker, Felix; Bayer, Christian; Eigel, Martin; Ladkau, Marcel; Neumann, Johannes; Schoenmakers, John: SDE based regression for linear random pdes (2017)
  4. Arnold, Douglas N.; Chen, Hongtao: Finite element exterior calculus for parabolic problems (2017)
  5. Chandrashekar, Praveen; Roy, Souvik; Vasudeva Murthy, A.S.: A variational approach to estimate incompressible fluid flows (2017)
  6. Chang, J.; Karra, S.; Nakshatrala, K.B.: Large-scale optimization-based non-negative computational framework for diffusion equations: parallel implementation and performance studies (2017)
  7. Chapman, S.J.; Farrell, Patrick E.: Analysis of Carrier’s problem (2017)
  8. Eigel, Martin; Pfeffer, Max; Schneider, Reinhold: Adaptive stochastic Galerkin FEM with hierarchical tensor representations (2017)
  9. Farrell, Patrick E.; Pearson, John W.: A preconditioner for the Ohta-Kawasaki equation (2017)
  10. Feischl, Michael; Tran, Thanh: The eddy current-LLG equations: FEM-BEM coupling and a priori error estimates (2017)
  11. Gunzburger, Max; Jiang, Nan; Schneier, Michael: An ensemble-proper orthogonal decomposition method for the nonstationary Navier-Stokes equations (2017)
  12. Harbrecht, H.; Peters, M.D.; Schmidlin, M.: Uncertainty quantification for PDEs with anisotropic random diffusion (2017)
  13. Hu, Kaibo; Ma, Yicong; Xu, Jinchao: Stable finite element methods preserving $\nabla \cdot \boldsymbolB=0$ exactly for MHD models (2017)
  14. Kahle, Christian: An $L^\infty$ bound for the Cahn-Hilliard equation with relaxed non-smooth free energy (2017)
  15. Lee, Jeonghun J.: Analysis of mixed finite element methods for the standard linear solid model in viscoelasticity (2017)
  16. Lee, Jeonghun J.; Mardal, Kent-Andre; Winther, Ragnar: Parameter-robust discretization and preconditioning of Biot’s consolidation model (2017)
  17. Meinlschmidt, H.; Meyer, C.; Rehberg, J.: Optimal control of the thermistor problem in three spatial dimensions. II: Optimality conditions (2017)
  18. Miklos Homolya, Lawrence Mitchell, Fabio Luporini, David A. Ham: TSFC: a structure-preserving form compiler (2017) arXiv
  19. Pinnau, René; Totzeck, Claudia; Tse, Oliver: The quasi-neutral limit in optimal semiconductor design (2017)
  20. Robert C. Kirby, Lawrence Mitchell: Solver composition across the PDE/linear algebra barrier (2017) arXiv

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