FEniCS

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 309 articles , 2 standard articles )

Showing results 1 to 20 of 309.
Sorted by year (citations)

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  1. Chen, Robin Ming; Layton, William; McLaughlin, Michael: Analysis of variable-step/non-autonomous artificial compression methods (2019)
  2. Cimrman, Robert; Lukeš, Vladimír; Rohan, Eduard: Multiscale finite element calculations in python using sfepy (2019)
  3. Crestel, Benjamin; Stadler, Georg; Ghattas, Omar: A comparative study of structural similarity and regularization for joint inverse problems governed by PDEs (2019)
  4. Dudziuk, Grzegorz; Lachowicz, Mirosław; Leszczyński, Henryk; Szymańska, Zuzanna: A simple model of collagen remodeling (2019)
  5. Feng, Xinzeng; Hormuth, David A. II; Yankeelov, Thomas E.: An adjoint-based method for a linear mechanically-coupled tumor model: application to estimate the spatial variation of murine glioma growth based on diffusion weighted magnetic resonance imaging (2019)
  6. Harding, Brendan: A Rayleigh-Ritz method for Navier-Stokes flow through curved ducts (2019)
  7. Kanzow, C.; Karl, Veronika; Steck, Daniel; Wachsmuth, Daniel: The multiplier-penalty method for generalized Nash equilibrium problems in Banach spaces (2019)
  8. Lee-Thorp, J. P.; Weinstein, M. I.; Zhu, Y.: Elliptic operators with honeycomb symmetry: Dirac points, edge states and applications to photonic graphene (2019)
  9. Li, Jiao; Ying, Jinyong; Xie, Dexuan: On the analysis and application of an ion size-modified Poisson-Boltzmann equation (2019)
  10. Miguel A.Rodriguez; Christoph M. Augustin; Shawn C.Shadden: FEniCS mechanics: A package for continuum mechanics simulations (2019) not zbMATH
  11. Printsypar, G.; Bruna, M.; Griffiths, Ian M.: The influence of porous-medium microstructure on filtration (2019)
  12. Rabault, Jean; Kuchta, Miroslav; Jensen, Atle; Réglade, Ulysse; Cerardi, Nicolas: Artificial neural networks trained through deep reinforcement learning discover control strategies for active flow control (2019)
  13. Sarshar, Arash; Roberts, Steven; Sandu, Adrian: Design of high-order decoupled multirate GARK schemes (2019)
  14. Šišková, K.; Slodička, M.: Identification of a source in a fractional wave equation from a boundary measurement (2019)
  15. Vasilyeva, Maria; Chung, Eric T.; Efendiev, Yalchin; Kim, Jihoon: Constrained energy minimization based upscaling for coupled flow and mechanics (2019)
  16. Attia, Ahmed; Alexanderian, Alen; Saibaba, Arvind K.: Goal-oriented optimal design of experiments for large-scale Bayesian linear inverse problems (2018)
  17. Badia, Santiago; Martín, Alberto F.; Principe, Javier: \textttFEMPAR: an object-oriented parallel finite element framework (2018)
  18. Bal, Guillaume; Hoffmann, Kristoffer; Knudsen, Kim: Propagation of singularities for linearised hybrid data impedance tomography (2018)
  19. Bänsch, E.; Karakatsani, F.; Makridakis, C. G.: A posteriori error estimates for fully discrete schemes for the time dependent Stokes problem (2018)
  20. Baumann, Manuel; Benner, Peter; Heiland, Jan: Space-time Galerkin POD with application in optimal control of semilinear partial differential equations (2018)

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