dolfinx
FreeFem-sources
dolfinx | FreeFem-sources | |
---|---|---|
18 | 1 | |
767 | 773 | |
3.8% | 1.8% | |
9.7 | 3.9 | |
4 days ago | 7 days ago | |
C++ | C++ | |
GNU Lesser General Public License v3.0 only | GNU General Public License v3.0 or later |
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dolfinx
- What's your main programming language?
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rodin alternatives - mfem and FreeFem-sources
7 projects | 8 Mar 2023
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Learn PDE constrained optimization
One thing that is a pain when learning this stuff is that actually performing the optimization requires a good understanding of the numerical discretization of PDEs. Finite elements are a natural choice because it is very easy to characterize the adjoint with this formulation. There are some good free tools that you can use to actually learn and do some computations yourself. The first is hIPPYlib (paper, code), which is built on top of FEniCS (link), for which there are many good tutorials. Beware trying to install this on Windows though. You will need to work in Docker or in Ubuntu via Windows Linux Subsystem.
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Open source FEA tools instead of ANSYS Workbench and APDL
If you're ok with coding, fenics is a solid place to start. Also if you're comfortable with coding, openfoam is FVM, rather than FEM, but it can handle solidmechanics.
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Eighty Years of the Finite Element Method: Birth, Evolution, and Future
> FEniCs made FEM so easy
https://fenicsproject.org/
Indeed, was blown away when I saw it for the first time over a decade ago, compared to the convoluted C++ FEM libraries I had seen before that.
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Best Python package(s) to solve PDEs numerically?
Have you looked at FEniCS? Pretty much everything else I'm aware of is probably overkill (e.g., MOOSE in C++, HYPRE's Python bindings, etc.)
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Open-source FEA software
FEniCSx is quite good.
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The Julia language has a number of correctness flaws
You mean Python? For many research tasks it's fine. High level libraries let you define your computation in a minimal amount of code. FEniCS is a great example of this - underneath it compiles the abstracted high level stuff to calls to low-level libraries that do the heavy lifting. For many applications you can just write vectorized code with Numpy that performs well, or use Numba to JIT what you can't vectorize. For some tasks, however, you need interfaces that don't exist in the high level libraries, and that was the case for me.
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What's a good book to learn to numerically solve ODEs and PDEs in python?
I just came across FEniCSX. I’m not sure if it’s what you want but here’s the description:
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Okay, let's end this Tabs vs Space debate once and for all
Fenics: Very popular finite element framework “UseTab: Never” https://github.com/FEniCS/dolfinx/blob/main/.clang-format
FreeFem-sources
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rodin alternatives - mfem and FreeFem-sources
7 projects | 8 Mar 2023
What are some alternatives?
Gridap.jl - Grid-based approximation of partial differential equations in Julia
getfem - Mirror of GetFEM repository
mfem - Lightweight, general, scalable C++ library for finite element methods
dealii - The development repository for the deal.II finite element library
taichi - Productive, portable, and performant GPU programming in Python.
feelpp - :gem: Feel++: Finite Element Embedded Language and Library in C++
ModelingToolkit.jl - An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automated transformations of differential equations
Ascent - A fast and flexible C++ simulation engine and differential equation solver.
pykokkos - Performance portable parallel programming in Python.
libmesh - libMesh github repository
CoreNeuron - Simulator optimized for large scale neural network simulations.