Gridap.jl
dolfinx
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Gridap.jl | dolfinx | |
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2 | 18 | |
635 | 648 | |
1.6% | 4.8% | |
9.2 | 9.6 | |
4 days ago | 6 days ago | |
Julia | C++ | |
MIT License | GNU Lesser General Public License v3.0 only |
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Gridap.jl
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Best free/open source CAS ?
Another I've been working on learning is Julia, which aims to use a syntax very similar to how you'd write it mathematically, and I like being able to include units in calculations using the unitful.jl package, and there are FEM packages available like Gridap.
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[Research] Input Arbitrary PDE -> Output Approximate Solution
PINN methods are absurdly slow (DeepXDE is about 10,000x slower than an ODE solver for example, while using implicit parallelism vs serial ODE solver) but they are flexible. So ModelingToolkit.jl has alternative options, like DiffEqOperators.jl takes the same specification and generates ODESystem and NonlinearSystem problems via finite difference discretizations (known as "method of lines"). There's a (pseudo-)spectral part of the interface coming relatively soon as well, with GridAP.jl integration for FEM coming soon. So this is made to be a universal arbitrary PDE -> approximate solution interface which is generic to the method and solving process.
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
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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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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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
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What are some best open source FEA solvers that one can test their new element implementation in?
To add to OpenSEES, dealii or FEniCS.
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[Research] Input Arbitrary PDE -> Output Approximate Solution
The closest to this is probably FEniCS, which is nice but directly tied to the FEM implementation, and thus is not method and solver agnostic.
What are some alternatives?
mfem - Lightweight, general, scalable C++ library for finite element methods
taichi - Productive, portable, and performant GPU programming in Python.
DifferentialEquations.jl - Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equations (SDEs), delay differential equations (DDEs), differential-algebraic equations (DAEs), and more in Julia.
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
ApproxFun.jl - Julia package for function approximation
DiffEqOperators.jl - Linear operators for discretizations of differential equations and scientific machine learning (SciML)
pykokkos - Performance portable parallel programming in Python.
FourierFlows.jl - Tools for building fast, hackable, pseudospectral partial differential equation solvers on periodic domains
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.
NeuralPDE.jl - Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation