The FEniCS Project Version 1.5

By M. Alnæs, Jan Blechta, Johan E. Hake, A. Johansson, Benjamin Kehlet, Anders Logg, C. Richardson, Johannes Ring, Marie E. Rognes, Garth N. Wells. 2015

2,471 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computational Theory and Mathematics, Numerical Analysis

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Citations in the corpus

Coupling damage and plasticity for a phase-field regularisation of brittle, cohesive and ductile fracture: One-dimensional examples (2017, methodology): “The code has been written using the FEniCS library [9,50] for finite elements and PETSc [14–16] for linear algebra and bound-constrained solvers.”
Phase-field modeling of anisotropic brittle fracture including several damage mechanisms (2018, neutral): “The ﬁnite element discretization relies on the FEniCS ﬁnite element library [53, 54], linear interpolation has been chosen for the displacement and damage ﬁelds.”
Modeling the Dynamics of PDE Systems with Physics-Constrained Deep Auto-Regressive Networks (2019, methodology): “ierent trajectory, making this a dicult system to predict accurately. Figure 9: 1D viscous Burgers’ equation simulations for four various initial conditions solved using Fenics nite element package [70]. The auto-regressive model used for the 1D Burgers’ system is similar to the one used for the K-S”
MFEM: a modular finite element methods library (2019, background): “tion 8). MFEM’s main capabilities and their corresponding sections in the paper are outlined in the following text. MFEM is distinguished from other nite element packages, such as deal.II [6], FEniCS [2], DUNE [16], FreeFem++ [47], Hermes [83], libMesh [58], FETK [51], NGSolve [79], etc., by a uniqu”
trlib: a vector-free implementation of the GLTR method for iterative solution of the trust region problem (2016, background): “We discuss algorithmic and numerical choices made in trlib that we have found to help improve performance and stability.”
Parallel Physics-Informed Neural Networks via Domain Decomposition (2021, background): “Additionally, it also provides interactivity with other discretization-based solvers such as Fenics [25].”
MOOSE: Enabling Massively Parallel Multiphysics Simulation (2019, background): “, [20, 21, 22]), but MOOSE is a common platform for creation of comprehensive multiphysics applications that are inherently shareable and can be coupled without modification.”
Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantification (2018, methodology): “element formulation implemented in FEniCS [61] with third-order RaviartThomas elements for the velocity, and fourth-order discontinuous elements for the pressure.”
An efficient, partitioned ensemble algorithm for simulating ensembles of evolutionary MHD flows at low magnetic Reynolds number (2017, neutral): “P 2 ) and the ﬁnite element software package FEniCS [5].”