BoundaryValueDiffEq.jl
fortran-lang.org
| BoundaryValueDiffEq.jl | fortran-lang.org | |
|---|---|---|
| 1 | 16 | |
| 40 | 126 | |
| - | - | |
| 9.3 | 8.1 | |
| 6 days ago | over 1 year ago | |
| Julia | HTML | |
| GNU General Public License v3.0 or later | MIT License |
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BoundaryValueDiffEq.jl
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Old programming language is suddenly getting more popular again
This isn't theoretical too, here's an actual user who opened an issue where their MWE was using quaternions:
https://github.com/SciML/BoundaryValueDiffEq.jl/issues/52
This is how I found out it worked in the differential equation solver: users were using it. The issue was unrelated (they didn't define enough boundary conditions), so it's quite cool that it was useful to someone. It turns out the quaternions have use cases in 3D rotations:
https://en.wikipedia.org/wiki/Gimbal_lock
which is where this all comes in. Anyways, it's always cool to learn from users what your own library supports! That's really a Julia treat.
fortran-lang.org
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Fortran, not as main language but I find it fascinating
A good starting point: https://fortran-lang.org/
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Are there any good FORTRAN IDEs on Linux?
Glad you're interested in Fortran. Not sure how old that book is, but take a look at https://fortran-lang.org/ for the latest news and best practices for modern Fortran (for example, there's now a standard library and package manager).
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HELP! New to Fortran
Install wsl and then in Linux install fortran gfortran. Fortran has moved on from 77 so find some f90+ stuff. https://fortran-lang.org/
- Fortran – Programming Language for high-performance computing
- Modern Fortran logo
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Ask HN: Which programming language communities seem most excited these days?
I cannot compare excitement across programming language communities, but I can say that there has been a lot of progress in the Fortran community, centered around the site https://fortran-lang.org/ and forum https://fortran-lang.discourse.group/ and surveyed in a preprint https://arxiv.org/abs/2109.07382. A package manager, standard library, and a compiler with a REPL (LFortran) are being developed. The Fortran standard has coarrays for parallelism, which several compilers have implemented.
A few years ago the main Fortran forum was comp.lang.fortran of Usenet, and although people did release tools and code, their efforts were scattered.
- What is the best material to learn Fortran and the philosophy behind it?
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Toward Modern Fortran Tooling and a Thriving Developer Community
The Fortran-lang group has written a paper summarizing their efforts, such as a web site https://fortran-lang.org/, a Fortran standard library, the Fortran Package Manager, Fortran Discourse, and LFortran.
- Fortran Newsletter: September 2021
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A Summary of Fortran [pdf]
Old content (this is from 1992) should have a year in the title. This is a summary of Fortran 77. The latest standard is Fortran 2018, and Fortran 202x is being worked on. Some features of Fortran 2018 have been implemented in compilers.
For current information on Fortran one should visit https://fortran-lang.org/ .
What are some alternatives?
DiffEqOperators.jl - Linear operators for discretizations of differential equations and scientific machine learning (SciML)
stdlib - Fortran Standard Library
SciMLBenchmarks.jl - Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R
lapack - LAPACK development repository
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
benchmarks - Fortran benchmarks
NeuralPDE.jl - Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
FABS - Fortran + Apache + BSD + sqlite = Web framework
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.