dolfinx
moose
| dolfinx | moose | |
|---|---|---|
| 18 | 4 | |
| 667 | 1,590 | |
| 4.0% | 1.9% | |
| 9.6 | 10.0 | |
| 4 days ago | 6 days ago | |
| C++ | C++ | |
| GNU Lesser General Public License v3.0 only | GNU Lesser General Public License v3.0 only |
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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
moose
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Open-source FEA software
GitHub Discussions as their forum: https://github.com/idaholab/moose/discussions
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Okay, let's end this Tabs vs Space debate once and for all
In our codebase ( https://github.com/idaholab/moose ) tabs are the first thing looked for in our continuous integration system. A tab will be an INSTANT fail.
- Nuclear Technology Devotes Entire Issue to MOOSE
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Build Loop from SCM not Updating
git --version # timeout=10 git --version # 'git version 2.32.0' git ls-remote -h -- https://github.com/idaholab/moose.git # timeout=10 Found 7 remote heads on https://github.com/idaholab/moose.git [poll] Latest remote head revision on refs/heads/master is: 1d1b703aef983bb75174883f145cd0d26f10d167 Done. Took 0.18 sec Changes found ```
What are some alternatives?
Gridap.jl - Grid-based approximation of partial differential equations in Julia
mfem - Lightweight, general, scalable C++ library for finite element methods
gtoolkit - Glamorous Toolkit is the Moldable Development environment. It empowers you to make systems explainable through experiences tailored for each problem.
taichi - Productive, portable, and performant GPU programming in Python.
elmerfem - Official git repository of Elmer FEM software
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
Kratos - Kratos Multiphysics (A.K.A Kratos) is a framework for building parallel multi-disciplinary simulation software. Modularity, extensibility and HPC are the main objectives. Kratos has BSD license and is written in C++ with extensive Python interface.
pykokkos - Performance portable parallel programming in Python.
FEM - ⚡🧠A finite element Python implementation
libmesh - libMesh github repository
preCICE - A coupling library for partitioned multi-physics simulations, including, but not restricted to fluid-structure interaction and conjugate heat transfer simulations.